Flight Test of Propulsion Monitoring and Diagnostic System · This report presents the results of the NASA program entitled "Flight Test of Propulsion Monitoring and Diagnostic System,"

NASA / CR--2002-211485

Flight Test of Propulsion Monitoring

and Diagnostic System

Steve Gabel and Mike Elgersma

Honeywell Laboratories, Minneapolis, Minnesota

_Jt_,

March 2002

https://ntrs.nasa.gov/search.jsp?R=20020061790 2020-06-24T06:34:13+00:00Z

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NASA/CR--2002-211485

Flight Test of Propulsion Monitoring

and Diagnostic System


Honeywell Laboratories, Minneapolis, Minnesota

Prepared under Contract NAS3--01105

National Aeronautics and

Space Administration

Glenn Research Center

March 2002

Trade names or manufacturers' names are used in this report foridentification only. This usage does not constitute an official

endorsement, either expressed or implied, by the National

Aeronautics and Space Administration.

NASA Center for Aerospace Information7121 Standard Drive

Hanover, MD 21076

Available from

National Technical Information Service

5285 Port Royal RoadSpringfield, VA 22100

Available electronically at http://gltrs.e-r¢.nasa.gov/GLTRS

Table of Contents

Executive Summary ........................................................................................................................ 1

Section

1.1

1.2

1.3

1.4

1.5

1.6

1. Introduction .................................................................................................................... 2

Background ................................................................................................................... 2

AGATE Program Results ............................................................................................. 2

Program Overview ........................................................................................................ 3PMDS Overview ........................................................................................................... 3

Relevance to Aviation Safety ........................................................................................ 4

PMDS Background Technical Data .............................................................................. 5

Section 2. Flight Testing ................................................................................................................. 6

2.1 Flight Test Aircraft ....................................................................................................... 62.2 PMDS Installation ......................................................................................................... 7

Section 3. Data Analysis ................................................................................................................. 9

3.1 Background ................................................................................................................... 9

3.2 Flight Test Overview .................................................................................................... 93.3 Data Collection ........................................................................................................... 10

3.4 Static Correlation Modeling ........................................................................................ 11

3.5 Linear Dynamic Modeling .......................................................................................... 17

3.6 Nonlinear Dynamic Modeling .................................................................................... 30

3.7 Data Analysis Conclusions ......................................................................................... 40

Section 4. Technology Roadmap .................................................................................................. 42

4.1 Modeling and Fault Diagnosis Algorithms Development .......................................... 42

4.2 Hardware and Software Development ........................................................................ 48

4.3 System Development .................................................................................................. 53

Section 5. New Technology .......................................................................................................... 56

Section 6. Conclusions and Recommendations ............................................................................ 57

Appendix

NASA/CR--2002-211485 iii

List of Figures

Figure 1-1.

Figure 2-1.

Figure 2-2.

Figure 2-3.

Figure 2-4.

Figure 3-1.

Figure 3-2.

Figure 3-3.

Figure 3-4.

Figure 3-5.

Figure 3-6.

Figure 3-7.

Figure 3-8.

Figure 3-9.

Figure 3-10.

Figure 3-11.

Figure 3-12.

Figure 3-13.

Figure 3-14.

Figure 4-1.

Figure 4-2.

Figure 4-3.

Figure 4-4.

Figure 4-5.

Figure 4-6.

Figure 4-7.

Figure 4-8.

PMDS .......................................................................................................................... 4

Flight Test Aircraft ...................................................................................................... 6

PMDS Flight Test Arrangement ................................................................................. 7

PMDS Hardware ......................................................................................................... 7

PMDS Installation ....................................................................................................... 8

PMDS Hardware Arrangement ................................................................................. 10

EGT Fault Model Results, Flight 44 ......................................................................... 13

CHT2 Measured Data and Modeling Data ................................................................ 14

CHT Fault Model Results, Flight 41 ......................................................................... 15

CHT Fault Model Results, Flight 44 ......................................................................... 16

Engine Bay Temperature Sensor Fault Model Results, Flight 44 ............................. 17Linear Dynamic Model Data ..................................................................................... 20

Linear Dynamic Model Results, Flight 41 ................................................................ 22

Linear Dynamic Model Results, Flight 44 ................................................................ 26

Nonlinear Dynamic Model Data ............................................................................. 31

Nonlinear Dynamic Model Results, Flight 41 ........................................................ 33

Nonlinear Dynamic Model Results, Flight 44 ........................................................ 35

Nonlinear vs. Linear Model Results, Flight 41 ....................................................... 37

Nonlinear vs. Linear Model Results, Flight 44 ....................................................... 39

Modeling and Fault Detection ................................................................................... 42

Oil Pressure Failure Detection and Warning Diagnosis ........................................... 44

Modeling and Fault Diagnosis Algorithms Development Roadmap ........................ 47

PMDS Architecture ................................................................................................... 48

PMDS Hardware Architecture .................................................................................. 49

PMDS Hardware ....................................................................................................... 50

Hardware and Software Development Roadmap ...................................................... 52

System Development Roadmap ................................................................................ 55

List of Tables

Table 1-1

Table 3-1.

Table 3-2.

Table 3-3.

Table 3-4•

Table 3-5.

Table 3-6.

Table 4-1.

• PMDS Glossary ............................................................................................................ 5

Flight Tests ................................................................................................................... 9

Linear Model Data ...................................................................................................... 21

Linear Model Plots ..................................................................................................... 21

Nonlinear Model Data ................................................................................................ 32

Nonlinear Model Plots ................................................................................................ 32

Comparison Plots ........................................................................................................ 36

Oil Pressure Fault Modes and Corresponding Detection Tests .................................. 45

NASA/CR--2002-211485 iv

Executive Summary

This report presents the results of the NASA program entitled "Flight Test of Propulsion

Monitoring and Diagnostic System," performed by Honeywell and Aurora Flight Sciences. The

objective of this program was to build on the results of the propulsion monitoring and diagnostic

system (PMDS) technology developed by Honeywell under the NASA Advanced General

Aviation Transport Experiment (AGATE) program and apply them to the broader goals of the

NASA Aviation Safety program. The technical work included two flight tests using PMDS

equipment and analysis of flight test data. The target application for the PMDS is piston-engine-

driven general aviation aircraft.

The PMDS concept is intended to independently monitor the performance of the engine,

providing continuous status to the pilot along with warnings if necessary. Specific sections of

this data would be available to ground maintenance personnel via a special interface. The inputs

to the PMDS include the digital engine controller sensors and other sensors. At its present stage

of development, the PMDS monitors and records engine parameters and stores them into an

engine history database for subsequent processing by off-line diagnostic algorithms. At present,

the system does not compare the parameter values with engine norms to perform on-line

diagnostics and prognostics (this extended functionality would be added in future development).

Technological advances in sensing, processing, and software have resulted in more affordable

and more capable health monitoring technology. The application of health monitoring

technology to aircraft engines has tremendous potential given the complexity, harsh

environmental conditions, and natural degradation that this machinery exhibits. Benefits include

increased safety and reliability and reduced operating costs.

The technical work performed on this program provided the following key results:

• It demonstrated the ability of the PMDS to detect a class of selected sensor hardware

failures.

• It demonstrated the ability of the PMDS hardware to successfully model the engine for the

purpose of engine diagnosis. Not surprisingly, nonlinear dynamic models performed better

than linear dynamic models for the same number of inputs and states.

Future development work for an engine monitoring and diagnostic system should employ the

following elements:

• Engine/aircraft modeling should combine first-principles and empirical approaches.

Empirical methods can be used to calibrate unknown parameter values as needed.

• The monitoring and diagnostic system should employ additional inputs outside the engine,

such as aircraft speed, aileron, elevator, rudder, and flap settings, propeller pitch, etc.

• A prioritized list of engine faults is needed to guide the diagnostic development work.

The monitoring and diagnostic system should be able to gather input data from the full authority

digital engine controller (FADEC) and other systems in the aircraft over a digital avionics bus.

This data sharing capability will enable the use of more sophisticated models and will help tominimize the installed cost of the PMDS.

NASA/CR--2002-211485 1

Section 1. Introduction

This report presents the results of the NASA program entitled "Flight Test of Propulsion

Monitoring and Diagnostic System," performed by Honeywell and Aurora Flight Sciences. The

following sections present the detailed technical results along with a set of conclusions and

recommendations based on experience gained in performing the work.

1.1 Background

A propulsion monitoring and diagnostic system (PMDS) can provide valuable benefits to general

aviation users. The PMDS provides increased confidence in the propulsion system while in flight

for improved safety and provides valuable diagnostic data to ground maintenance technicians for

reduced maintenance costs. The target application for the PMDS is piston-engine-driven general

aviation aircraft. In this program, the PMDS technology developed by Honeywell under the

NASA Advanced General Aviation Transport Experiment (AGATE) program was extended

through additional flight testing and data analysis to demonstrate its current capabilities.

The purpose of the PMDS concept is to provide the pilot with engine health indications and to

inform the pilot when the engine requires preventative maintenance. By providing this

information before in-flight failure of the engine, it greatly enhances flight safety and provides

simplified engine diagnostics for the pilot. The technology developed under AGATE funding is

the core of a future fully functional PMDS. The hardware and software developed under AGATE

funding monitors and records engine parameters and stores them in an engine history database

for subsequent processing by off-line diagnostic algorithms. At present, the unit does not

compare the parameter values with engine norms to perform on-line diagnostics and prognostics

(this extended functionality would be added in future development).

1.2 AGATE Program Results

Honeywell was a participant in the AGATE Propulsion Sensors and Controls Work Package.

During 1999, Honeywell built the PMDS hardware, implemented the firmware that records and

preprocesses the flight data, and with the assistance of Aurora Flight Sciences Corporation,

performed an initial flight test. A simple engine model, developed to postprocess the flight test

data, verified that we could detect a failed (disconnected) exhaust gas temperature (EGT) sensor

signal. A shortfall in funding prevented further test flights under the AGATE program. However,

these results showed the significance of the core PMDS in that it provides both hardware and

software design guidelines for successfully interfacing the PMDS to general aviation aircraft.

Additional test flights and data analysis were performed on this 2001 NASA program to verifythat the design and integration of the core PMDS into the aircraft is a suitable base on which to

build the diagnostic capability.

NASA/CR--2002-211485 2

1.3 Program Overview

The objective of this program was to build on the results of the AGATE work and apply them to

the broader goals of the NASA Aviation Safety program. The technical work included two flight

tests using PMDS equipment and analysis of flight test data.

Our technical approach for the flight testing and data analysis consisted of four steps:

1. Collect data from a set of pertinent engine sensors during a baseline test flight (no

failures).

2. Compute an engine model based on a least-squares fit to flight test data.

3. During the second test flight, introduce sensor faults to test the diagnostic algorithm.

4. Postprocess the flight data to diagnose the faulty sensor/variable.

This program also included the development of a roadmap detailing the recommended next steps

in applying the PMDS technology to general aviation.

1.4 PMDS Overview

The PMDS is a separate subsystem designed to independently monitor the performance of the

engine, providing continuous status to the pilot along with warnings if necessary. Specific

sections of this data are available to ground maintenance personnel via a special interface. The

PMDS also provides a set of data for maintenance event prediction to be used by ground

personnel or for possible impending failure information to be displayed to the pilot. The PMDS

will continuously monitor its own performance to ensure its own integrity and capability.

The PMDS will be able to detect and diagnose the most common engine failures. The set of

failures to be detected will be defined in future development phases. A top level of the system is

shown in Figure 1-1.

The PMDS continuously monitors the performance of the engine in order to detect failures and

predict impending failures (prognosis). Failures and warnings of impending failures are indicated

to the pilot, and the collected engine diagnostic information pertaining to the failures and/or

warnings is also available to a ground maintenance technician. The AGATE PMDS development

effort defined the following performance goals for the system:

• Early detection time: The PMDS is intended to detect and indicate a warning of impending

failure at least 8 flight hours prior to failure.

• High probability of detection and coverage: The PMDS is intended to detect 90% of

impending failures in the engine.

The inputs to the PMDS include the full authority digital engine controller (FADEC) sensors and

other engine sensors. The outputs consist of the pilot warning display and the maintenancedevice interface. The maintenance device interface could be a hand-held interrogation and

display device that would allow maintenance personnel to determine the status of the engine (as

NASA/CR--2002-211485 3

ff

I"/

/

//

/I

I

I

l

tIIl1

PMDS

_\ di splay

\\

,,- ...... ----"FADEC l J'\, ..- --i"/'''"

x ground-based...... _ maintenance data

PMDS i-t ""---... F22S--2

I "_t I

_11 I I II I J

//

engine sensors / \and other inputs //

// maintenanceJ

"-... ./" technician's........ interface

PMDS System

Figure 1-1. PMDS

well as the PMDS itself) and define any necessary maintenance actions. Readout capability is

included in the on-board maintenance panel; however, some installations could forgo this panel,

depending completely on ground-based equipment that would receive data from the interface

port.

Sensor inputs will be received via digital interfaces from the FADEC, single-lever power control

(SLPC), and other digital systems on the aircraft, or via other sensors directly connected to the

PMDS. Examples of these sensor inputs are as follows:

• FADEC: exhaust gas temperature (EGT), cylinder head temperature (CHT), engine speed(RPM), oil pressure, etc.

• SLPC: engine power lever position, throttle command, propeller pitch command, etc.

• Other inputs via an avionics bus: air speed, pilot inputs to control surfaces, etc.

• Other sensors directly connected to the PMDS: vibration sensors, oil particle sensors, etc.

A glossary of terms relating to the PMDS is shown in Table 1-1.

1.5 Relevance to Aviation Safety

Technological advances in sensing, processing, and software have resulted in more affordable

and more capable health monitoring technology. The application of health monitoring

technology to aircraft engines has tremendous potential given the complexity, harsh

NASA/CR--2002-211485

Table 1-1. PMDS Glossary

Early Detection: System predicts that failure will occur in near future, lightswarning light.

Failure Detection: System detects that failure has occurred, lights failure light,(future implementations may take backup action if indicated).

False Alarm System indicates that failure has occurred, although in fact,indicated failure has not occurred.

Impending Failure Engine condition has changed so as to cause a failure in nearfuture.

Diagnostics The process by which a particular fault mode is indicated.

Detection Time The time between the occurrence of either an impending

failure (in the case of early detection) or a failure (in the caseof failure detection) and the corresponding failure or warningindication.

Accuracy RSS (Root Sum Square) value including scale factortolerance, linearity, offset and temperature effects.

Failures A failure is a fault which will require ground maintenanceaction to correct.

FADEC Full Authority Digital Engine Controller

SLPC Single Lever Power Control

EGT Exhaust Gas Temperature

CHT Cylinder Head Temperature

environmental conditions, and natural degradation that this machinery exhibits. Benefits include

increased safety and reliability and reduced operating costs.

The benefits of engine monitoring and diagnostics will be valuable to all segments of general

aviation, from the individual aircraft owner to large fleet operators. All of these potential users

share a common interest in having a capability to increase the availability of their aircraft

engines. At present, we don't know of any commercially available engine monitoring systems for

piston engine aircraft. However, these types of devices are currently offered as optional

equipment in at least one single-engine turboprop aircraft (the Hiatus PC-12). Hiatus offers an

"engine trend monitoring" option with the PC-12 as described at !l_t_t.p://www.pilatus-

aircraft.corn/2 ga commercial/framese! pcl2.htm.

1.6 PMDS Background Technical Data

Under the AGATE program, Honeywell prepared a set of technical documents for the PMDS

concept. These documents define the system requirements, modeling methods, and fault

detection and failure diagnosis methods. This body of information serves as a baseline for future

development of the system.

The PMDS concept is intended to meet standard commercial avionics integration requirements

that apply to the intended application configurations. The PMDS concept is also intended to

conform to the same environmental, electrical, and mechanical standards that apply to

comparable commercial avionics equipment.

NASA/CR--2002-211485 5

Section 2. Flight Testing

The flight testing was performed by Aurora Hight Sciences as described in the following

subsections. Additional information is presented in the Aurora flight test report in the Appendix.

2.1 Flight Test Aircraft

The flight tests were performed using Aurora's twin engine Cessna 0-2 Chiron aircraft, as

shown in Figure 2-1. This aircraft was equipped with a SLPC and FADEC controlling the front

engine (the rear engine was not involved in the flight testing). With the SLPC and FADEC,

electric servo actuators control the throttle and the prop governor; the pilot commands a single

thrust command. Electronic port fuel injection, electronic ignition system with several redundant

feedback loops controls mixture and thermal control (CHT, EGT, exhaust gas oxygen).

Figure 2-1. Flight Test Aircraft

The Chiron front engine (monitored during this test) is a Teledyne IO-360ES. The flight tests

were performed according to flight test plans developed jointly by Honeywell, Aurora, and

NASA. Flight test data was logged on the PMDS. After each flight, the PMDS data was

downloaded via serial interface to a PC and sent to Honeywell via the Internet for data reduction

and analysis.

To test the PMDS diagnostic concept, we simulated a sensor failure by temporarily

disconnecting some noncritical sensors on one of the flights. The flight test approach is shown in

Figure 2-2.

NASA/CR--2002-211485

non-critical sensor

pilot good ______

inputs "_ H failedFADEC Engine

criticalsensors

PMDS

sensordata for

modeling

Figure 2-2. PMDS Flight Test Arrangement

In addition, we noticed some unplanned intermittent sensor faults as described in Section 3 of

this report. This flight testing procedure had no effect on the engine's performance, since the

FADEC recognizes and accounts for faulty EGT signals.

2.2 PMDS Installation

The Honeywell PMDS was mounted in the cabin of the aircraft. Figures 2-3 and 2-4 show the

PMDS hardware and its installation in the aircraft.

Figure 2-3. PMDS Hardware

NASA/CR--2002-211485 7

Figure 2-4. PMDS Installation

NASA/CR--2002-211485

Section 3. Data Analysis

The flight test data files were received from Aurora Flight Sciences and prepared for analysis.

Several technical analyses were performed on the data using Matlab scripts. This technical

analysis work is described in the following subsections.

3.1 Background

The flight testing was performed by Aurora Flight Sciences in a manner similar to that used for

the earlier AGATE work. Aurora's twin engine Cessna 0-2 Chiron aircraft was used for these

tests. The Honeywell PMDS was employed with Aurora's FADEC and electronic SLPC, which

controlled the front engine in the aircraft. The PMDS collected various engine data and other

parameters made available directly from the FADEC via a digital communications link.

3.2 Flight Test Overview

Under this program, two flight tests of the PMDS were conducted. Data was collected at a

variety of altitudes and power settings over the operating envelope of the engine. A top-level

description of the two flight tests is presented in Table 3-1 below.

Table 3-1. Flight Tests

Profile: Flight 41

12ooo F i i / i '_ i i

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0 1000 2000 3000 4000 fi000 6000 7000 8000 9000

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Date: April 25, 2001

Setup: Baseline flight

Simulated Faults: none

Intermittent Faults: CHT2 and EGT4

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4000

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Profile: Flight 44

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Date: April 27, 2001

Setup: Add simulated faults

Simulated Faults:

• Engine bay temperature 2

• Mass airflow sensor

Intermittent Faults: CHT2, EGT1, and EGT4

'f r

8000 90O0

NASA/CR--2002-211485 9

The simulated fault in the mass airflow sensor was not used in the analysis because the sensor

data was not set up for communication to the PMDS. This was an oversight in the preparation of

the test plan, but it did not affect our ability to accomplish the objectives of the flight test and

analysis work. The flight test plans and flight test engineer reports are presented in the Appendix.

3.3 Data Collection

For this project, the PMDS collected engine performance data from the engine FADEC via a

digital communications link. The hardware arrangement is shown in Figure 3-1. (No directly

connected engine sensors were used in this project.)

FADECController

Sensors

SerialInterface

::::::::::::::::::::::::::::::::::::::::::::::::::::::::::

I

LongT_m

Memo_

Central

DiagnosticProcessor

and Memory

Input/OutputController

and

SignalConditiomng

_/////_

y///_

///////_

OutputPort

DiagnosticOutput

Figure 3-1.

PMDS

PMDS Hardware Arrangement

The current PMDS hardware/firmware records the values of 25 variables: time, pressure altitude,

air pressure, fuel pressure, oil pressure, fuel temperature, oil temperature, bay 1 temperature, bay

2 temperature, six EGTs, six CHTs, outside air temperature, manifold air pressure, air charge

temperature, and engine RPM. The 25 variables are measured at 5 Hz. To average out noise and

to fit all the data from many hours of flight into limited memory, the data is reduced in the

following way:

NASA/CR--2002-211485 10

1. Over each 51-second time window, a least-squares-error linear fit is made to each

variable's time history in that window.

2. The slope and intercept of this linear fit is saved, together with the maximum andminimum values of each variable in this window.

A list of the sensor data collected from the FADEC is presented in the Appendix.

3.4 Static Correlation Modeling

A static correlation model can be used to estimate the value of one or more sensors using

measured data from other sensors. While this is not a dynamic model per se, it does employ

measured data across a wide range of operating conditions and is capable of producing a goodestimate of the sensor values of interest. These estimated values can then be compared to

measured sensor data for monitoring and diagnostic purposes. In practice, it would be beneficial

to create several static correlation models using different combinations of inputs in case any one

of the inputs is faulty (thereby causing all of the outputs to be invalid). This approach would

enable the system to check each model to determine which input is faulty.

3.4.1 Static Correlation Modeling Approach

The form of the static correlation model is

y =f(x)

where

y _ R p = output (estimates of desired sensors)

x _ R n = input (measured data for other sensors, including their derivatives)

The model (matrix A) is computed using a least-squares approximation to measured data. For

estimating the five EGT states, matrix A is computed from

AX= Y

where

[ xl0)''''x'(k) R,,×kX= i i

[x,,O).",x,(k)

EGT. 0)"" EaT, (k ) ]

y = : : ] _ R p_s (In this example, p = 5 because one of

G-TpO) Tp--(k)] the EGTs was bad in both test flights.)E ... EG

k = number of samples (period of time) over which the estimate is desired

The correlation matrix A is computed from measured data as follows:

A = yxr(xxr) -l

NASA/CR--2002-211485 11

3.4.2 Static Correlation Modeling of EGT Sensors

A static correlation model for the EGT sensors was computed using the data from Flight 41 as

described above. This model was then applied to measured data from Flight 44 (using sensors

other than the EGTs as input data). The resulting estimated EGT data compared very well against

the actual measured EGT data for Flight 44, as shown in Figure 3-2. As mentioned earlier, EGT1

in Flight 44 showed some intermittent bad data (i.e., an intermittent fault). This bad data is

clearly visible when compared to the estimated values shown in Figure 3-2.

3.4.3 Static Correlation Modeling of CHT Sensors

As mentioned earlier, sensor CHT2 showed some intermittent bad data in both Flight 41 and

Flight 44. The periods of bad data are shown in the top and middle subplots of Figure 3-3. For

modeling purposes, the good data from each flight was combined to create the model. This

model input data is shown in the lower subplot of Figure 3-3. A static correlation model for the

CHT sensors was computed using this data. This model was then applied to measured data from

Flight 41 (using sensors other than the CHTs as input data). The resulting estimated CHT data

compared very well against the actual measured valid CHT data for Flight 41, as shown in

Figure 3-4. The intermittent fault in CHT2 is clearly visible when compared to the estimated

values shown in Figure 3-4. The same analysis was done for Flight 44, which produced similar

results as shown in Figure 3-5.

3.4.4 Static Correlation Modeling of Engine Bay Temperature Sensors

Both engine bay temperature sensors provided valid data in Flight 41. For Flight 44, baytemperature sensor 2 was disconnected to simulate a fault condition. A static correlation model

for the bay temperature sensors was computed in the manner described earlier (using measured

data from Flight 41 ). This model was then applied to measured data from Flight 44 (using

sensors other than bay temperature sensors as input data). The resulting estimated bay

temperature sensor 1 data compared very well against the actual measured data for Flight 44, as

shown in the top subplot of Figure 3-6. The simulated fault condition in bay temperature sensor 2

(consistently low) is clearly visible when compared to the estimated values shown in the lower

subplot of Figure 3-6.

NASA/CR--2002-211485 12

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G"ov

.o

0 2000 4000 6000 8000

time (sec) PIV[_ Ftight44

ov

o

1000

8OO

6O0

4O0

200

0

EGT6 measured (solid), EGT6 estimated (dotted)1200 , ,

ii

i r

....... _L_ - - - _ ...... ' ..... _"

t_ _ r r

!

--t ......................

1!

_ _ _,." ...................

0 2000 4000 6000 8000

time (sec) F_DS Right44

Figure 3-2. EGT Fault Model Results, Flight 44

NASAJCR--2002-211485 13

700i

i

i

600 .............. '-i

i

o

_ 500

400

CHT2-Flight44 measured (solid)700 i i i

i I i i

i i i i

! ,

600 : ' ' 'i i i

i i i

i

i i

i t i i i, ,_fi ,

i

i .

300 I _ i

0 1000 2000 3000 4000 5000 6000 7000 8000 g000

CHT2 data used for estimation (dotted)700 , r _ ,

600

o t _ i

i i i

500 ....... _ ...... ,...... _....... _ ...... ,....... ,....... _....... ,......

400 _______e ....... , , _ r, ,

i_ i i p i i300 I _ I _0 1000 2000 3000 4000 5000 6000 7000 8000 9000

time (sec)

Figure 3-3. CHT2 Measured Data and Modeling Data

NASA/CR--2002-211485 14

CHT1 measured (solid), CHT1 estimated (dotted)700

600 ........................_-.

o500 ...................

_J _*_ ! k _ .j, \ _ ,_.,_J',

400 [ .... ±v__ _ , .....- ....... i

! it

300 ' '0 2000 4000 6000 8000 10000


600G"o

500

4O0

CHT2 measured (solid), CHT2 estimated (dotted)

3O00 2000 4000 6000 8000 10000


600 .........................

o5oo ............... i..........

-- _ _-_,"- _ _ L ....400

3000

i

q

I d

2000 4000 6000 8000 10O00

600 ......................

2...o

500 ................. : ....

I_ 400 _- - - _ ............ -_'_± _ _- . .....I

3000

4 I

I

I t

2000 4000 6000 8000 10O00


600 .........................

ov

500 .... _ .......... •..........

_" 400 ....................- -_ - _ , _t_

:300 ' ' ' '0 2000 4000 6000 8000 10000

time (sec) PIVlDSRight41

600

o

_ 4oo


......................... I

........................ i

L I I I

300 i i 1 1

0 2000 4000 6000 8000 10000

t_n_ (s_) _ Fl_:jht41

Figure 3-4. CHT Fault Model Results, Flight 41

NASA/CR--2002-211485 15

ov

7OO

CHT1 measured (solid), _HT1 estimated (dotted)

600 ........................

500 - - -' ................

400 _

i

300" I i _,0 2000 4000 6000 8000


700 --

I600,_ ...................

2

500

t-: 400 ---J .................

3000

¢ ,! b

2000 4000 6000 8000

CHT5 measured (solid),700

3000

6O0

o

50o

_- 400

"HT5 estimated (dotted)

2000 4000 6000

time (sec) PMDS Right44

8000

600

o

= 500

I_" 400

CHT2 measured (solid), CHT2 estimated (dotted)700 I

i,i

,iIIi

_i i -?

i ' I

300 _ ' _ -0 2000 4000 6000 8000


700 _ I

600 .... ; ..... _ ............

, ,

._ 500 ........... " .............

400 - - - _ ........ -' ....... --"_-_'_-- -

/

300 10 2000 4000 6000 8000

6O0

o500

I_" 400


i

......................

........................

- - - _ ..... _'_-_-"- -%_.,_x_- -

300 _ _ _0 2000 4000 6000 8O00

time (sec) PIVIDSFlight44

Figure 3-5. CHT Fault Model Results, Flight 44

NASA/CR--2002-211485 16

BayTemp l measured (solid), BayTemp l estimated (dotted)

400 I _,

350 .................................................i i

i i

300 - -i

V..... !...... !.................................._°°- .... i ..... i...................... i................15o_...... ......................................

100 ..... , ...... _

50..... :.................... .... _.................0 1 L

0 1000 2000 3000 4000 5000 6000 7000 8000

time (sec) PIVDS Flight44

BayTemp2 measured (solid), BayTemp2 estimated (dotted)

400 E i t i i

]

350 ...............................................................i E i i i

E p i i

^^^ L_ , , _ , _L ......LSUU - _ - _'_;. ..... _ ....... L....... _ ............. , ---"

250 ............... ,............ _ ................. '- .......o

200 ................................................... ? .......d

_'150

100

50

0

................ r...............

d

.............. 1"- .......

q

i

T ir .......

i iI I

0 1000 2000 3000 4000 5000 6000 7000 8000


Figure 3-6. Engine Bay Temperature Sensor Fault Model Results, Flight 44

3.5 Linear Dynamic Modeling

A linear dynamic model can be used to estimate the value of one or more sensors using a past

history of measured data from other sensors. This type of model is capable of producing a goodestimate of the sensor values of interest. These estimated values can then be compared to

measured sensor data for monitoring and diagnostic purposes.

NASA/CR--2002-211485 17

3.5.1 Linear Dynamic Modeling Approach

The linear dynamic model is constructed in conventional state-space form, with a state equation

and an output equation. The form of the dynamic model is as follows:

_=Ax+Bu

y = Cx + Du

where

x_ R" is the state vector

u _ R'" is the input vector

y _ R _' is the output vector

A _ R "×", B _ R ''_' are the system matrices

Given a set of measured inputs over a specified time period, this model can be used to estimate

the values of all modeled system states and outputs. In our analysis work, we generally did not

compute the C and D matrices. (In a commercialized system, some states could be ignored and

various outputs of interest could be defined and computed for monitoring and diagnostic

purposes.)

Three dynamic models were developed using measured data from Flights 41 and 44. To

compare the models, each used the same set of inputs and state variables. Any sensors that hadsimulated or intermittent faults were not used in the model.

Analysis of the flight test data showed that both test flights exhibited nonlinear characteristics

(i.e., engine RPM was a nonlinear function of SLPC setting). For this reason, the dynamic

models were linearized over the selected operating range. The resulting models were linearized

about a nominal "trim" condition in the following form:

(_: - x_ )= A(x - x _, )+ B(u - u_ )

Since the X_-imterm is assumed to be zero, the above expression can be rewritten as

:_ = Ax + Bu- (Ax_i m + But_)

The last term, (Ax_ + Bu_m _), can be approximated by an additional "bias" input (set to 1.0 in

the u vector), thereby giving the conventional form:

2= Ax + Bu

NASA/CR--2002-211485 18

Themodelwascomputedusinga least-squaresapproximationto measureddata.model is

where

=[A,83*[X;U]

k -- number of samples (period of time) over which the estimate is desired

The form of the

= [k(tj ) ..... k(t_ )] e R ''_k

-x(t,) ..... x(tk) ] R,,,+.,,×k

[X;U]= u(ti) ..... u(tk)J

The system matrices [A, B] were computed as follows:

sc[x;u[[[x;vIx;v l'

3.5.2 Model Data Preparation

The linear dynamic models were developed using the data collected in the two flight tests. The

input data for the models was prepared as follows:

1. Measured data from Flight 41 and Flight 44 were placed into data files; this data was

described in subsection 3.3.

2. Other pertinent data not collected automatically by the PMDS was added to the data files

manually. This manually added data included the SLPC settings and cowl flap data

collected from the flight test engineer's reports.

3.5.3 Linear Dynandc Model Development

Three linear dynamic models were developed using the technical approach described above.

These models are as follows:

• Model 41 created from Flight 41 test data

• Model 44 created from Flight 44 test data

• Model 4144 created from a combination of Flight 41 and Flight 44 test data

These models were developed from the flight test data as described above. The portions of the

flight tests used in developing the models are shown in Figure 3-7.

The flight test data (inputs and states) used in developing the models is shown in Table 3-2.

Altitude slope, or rate of change, was used as a substitute for elevator setting (i.e., to indicate the

load on the engine). This was done because we had no way to collect elevator position or pilot

NASA/CR--2002-211485 19

$v

OJ

<

Flight 41 Altitude

12000 .... r ......... ,....... , ..... / ..... ,-- -, ..........

ortion of flight used/_or dynam+c modeling10000• .... r ...... , : , : _, , - ......

|

i _ ] i :

8000 1 .... , _ ,..........r+.... ...._........++......, I " I I i_

, I ./ I j \

i / I i i ".6000 _ - ...... _- +...... ,+ - - -+..... ,- .... "+,...- ....

, / _ i i : "

+ i - i i ! s

4000 +="=>+-"=+=+-'+-+ - + .... ,.......... + - - _ - +i i i i ! r

i :+ i i i J \_, i

2000 ..... +__ _ _._,L ,_......... ,_ ....... _ ....i / t + i _" ---i +? i , i , ".

i : , i ii J I J I " ....

0 1000 2000 6000 7000 8000 90003000 4000 5000


Flight 44 Altitude

L ] ...... I [ I +../.. /. [/_ . I [ I12000 r ...... r ,...... ,....... , .... ,- -_'-" -"- ++'__ =_'<- - -, ...... ,......

_ . _ , f , '\ , .

[- : : _, portion of flight used _ dynamlic modeling J : /

'°°°°I, i ......!Ii i / i i _1 i ', i / j j

,i _ / ;_ I, II+-m600or ........... ,-...... + /.... +-...... ,...... +..... ,--+_-4--,_.....

i + , i / + , + , _ I , !

• b i i i i ' i' / , \

< 4000 _-................ +"..++=_...+...z-+c++.z............................. #4- ........ -I

, , , .... ,,I , [2000 ...... '_ .... J_-- - " ..... " ............ " .... "-+-_.--i ...... -_

' ,,/ ..... \, /• i i i i r

0 -------_"-"7---''/ i , i i + '4 i0 1000 2000 3000 4000 5000 6000 7000 8000 9000

time (sec) PMDS Flight44

Figure 3-7. Linear Dynamic Model Data

commands electronically. Similarly, we did not have a means to electronically collect airspeed

data, and that data was not collected manually during the flights. However, some of the other

sensor data effectively serves the same purpose (i.e., a combination of altitude, altitude rate, and

SLPC setting).

Pressure altitude and air charge temperature inputs track other inputs either directly or inversely,

but were used in order to produce a better model.

NASA/CR--2002-211485 2O

3.5.4 Linear Modeling Results

The above linear models were used to simulate a flight using initial conditions taken from either

Flight 41 or Flight 44. Simulated results were compared with actual measured flight test data.

The results are shown in Figures 3-8 and 3-9. The plots in Figures 3-8 and 3-9 were prepared as

described in Table 3-3.

Table 3-2. Linear Model Data

Inputs States Not Used Bad Sensors

Altitude

Altitude Slope

Outside Air Temp

SLPC

Cowl Flaps

Air Charge Temp

Pressure Altitude

(Dummy Input)

Fuel Pressure

Oil Pressure

Fuel Temperature

Oil Temperature

EGT2

EGT3

EGT5

EGT6

CHT1

CHT3

CHT4

CHT5

CHT6

Manifold Air Pressure

Engine RPM

Kollsman

Bay 2 Temperature

Bay 1 Temperature

EGT1

EGT4

CHT2

Figure

3-8

3-9

Table 3-3. Linear Model Plots

Flight

41

44

Models Used

41 and 4144

44 and 4144

Plotted Data

Measured data: heavy line

Linear Model 41 data: dashed

Linear Model 4144 data: solid



Linear Model 4144 data: solid

NASA/CR--2002-211485 21

Flight41 Altitude and SLPC setting

12000[_-....... _,!..............................................." " "J_" " "_ •'" "i i / i ! , i

_ 80o0) ..... :........ ........ I-- - _ ......... _ ..... _.... __\_ .....

<

4000 - - - ....... ;........---...........-r........ _ ....... _ .... r ....... r -'_:- - ._

i i i i i i

0 i 4 i i i , i

0.5_LI I r I I L 0

0 1000 2000 3000 4000 5000 6000 7000 8000

FuelPress

................• il

LI [ I

0 1000 2000 3000 4000 5000 6000 7000 8000

OilPress7O

i/ i i i

_/_" i I J

65 ....... ,........ .... .... - - -v I _ r

r i i i .........._, 6o_-............. ..... ............

r

55 I i j

0 1000 2000 3000 4000 5000 6000 7000 8000

FuelTemp

............... .......!.......!.......!......280 _-..... ,....... "- -

i i I i/ -- measured data ' _'" .... "_'- , _

i_" 275_ ___ Right41model =:..... _ ...... _ ....... , - - " " , - .....

270 I I = _ I _ L0 1000 2000 3000 4000 5000 6000 7000 8000


Figure 3-8. Linear Dynamic Model Results, Flight 41

NASA/CR--2002-211485 22

360

350.=_=

@

_- 340

f330 L

1060

OilTemp

I I i

r "¢ , _ ,,

i i i i

11300 2000 3000 4000 5000 6000 7000 8000

EGT2

v 1040 ..............¢ 1020 ..........

1000 .......................

980............. ,...........

960 t

1050

1000==

-£- 950

9O0

0 1000 2000

J

' i

i i i

J I I

3000 4000 5000 6000 7000 8000

EGT3

i i i i

i I i I

........ , ............ 7 ........ T ....... T- -

i i Ilk ----

................... r _ i

i i

I I I

1040

_. 1020o

1000

_ 980

_- 96o

940

1000 2000 3000 4000 5000 6000 7000 8000

EG T5

I ......... L ....... L ......

........ i............ -_ ...... _r ...... _ ...... _ -

i J I I /...............1-- measured data t ._ l(; -- _v_ _,'_'_A

, l _,/ I I

Rlght"l model .... t 7 _- _ _ ..... _'" _ -'r- -"- -'--- r ......

Rights41&44 model V ' _ _ i _I I t I I I

1000 2000 3000 4000 5000 6000 7000


8O0O

Figure 3-8. Linear Dynamic Model Results, Flight 41 (continued)

NASA/CR--2002-211485 23

EG T61020 , , _ , r

! ' ....I I I i I

1000 ...... '- _ ....of_ , ..... , ........... , .... _ ...... T ..... _ .......

980, - ........... - ...... _ ....... _ ..... ,- .... _-......

...... ,.... V .... ........ 7--- .......920' i I J

ov

m'-I

0 1000 2000 3000 4000 5000 6000 7000 8000

CHT1

460 ii [ D# _ I I

400 L i , j _ _

0 1000 2000 3000 4000 5000

CHT3460 I ! I

I I I J "

v_o_(_ 440 .............. It_................. _ !II.... %% _- III -_ rI .... # "

420 , , _c q - -I I ", I I

_X

.e 4uu- ................ _....... -_....... _ .......I-- I I I I

I I I I

I I I

380 I i i I0 1000 2000 3000 4000 5000

6000 7000 8000

6000 7000 8000

CHT4

460[ I I I I ' ' i

44o : - _, -- _....... _......! _ i i

i ,t.

= 420 ........ ,:........ ,"--- -_ _-', , i-'_J-l- - -- -'_= "-'_'_- _- - .....filler' .......... ' ' 1-- measured data / "'" " 'i "_. 400 Right41 model [ .... -_.................

Rights41&44 model ! _ i '".." ',380 I i i l i0 1000 2000 3000 4000 5000 6000 7000 8000

time (s_c) _ Right41


NASA/CR--2002-211485 24

CHT5

390r _ r _ _ _

/ ', t I t t_ t t t i

"_ .... 1_ .... L .......

oooV ...............................,,,,..,oo i i A i J i i

," r t t / J t tt _ t t t t

._ 370_-....... . ...... -,_'='_-%-.-L.-_-'_o ---:K4 -'_'_- - .... " l] - - -_

i ' i " i_ i t I i

_. 360 ......................,,, ,,, _,,, _,,, ,_, ,,_ _, --"350 k . R i

0 1000 2000 3000 4000

CHT6430

5000 6000 7000 8000

C" 420

9 410:3

400

E

:3(/)

_r

[ I I I [ l

ti I I _ i

i I I i_ i

...... ,....... _--_ ..... _...... _..... ,.r -_ - - - • ...... T ......i i_ i f I i i i

i /t\, "'- LJv/I \ %.. ,i' \ _, Ii \ _ \ ' I ",

390 ............... ,....... n ....... • .............. r-_ .........

380 _ I I _ I I

0 1000 2000 3000 4000 5000 6000 7000 8000

ManifoldAirPr

100 I i I [ I I It _ t t t t t

I i_ t t t

90 ....... _........ _-""_--_- - _ ....... _ ....... _ ....... _ ....... L .... _ _] t t i t t /

r t _ t i

80 ' _ ' ' _ ' •........ I........ I .......... _ ....... 7" ...... T ....... _-- --

70 , _ .......... _---,,- - '- - .... '-

60 I I _ '0 1000 2000 3000 4000 5000 6000 7000 8000

30OO

" 2800

"o 2600

GO2400

.c_

2200

2000

EngineRPM

........ , ....... _....... _ --..-,_h,._- - -1....... _- r-_ _ _Ir_"__ _,, _ B _ _ _ _

I Rights41&44 model ' : : : :I I I I _ I i

1000 2000 3000 4000 5000 6000 7000 8000

time (sec) RVDS Right41

Figure 3-8. Linear Dynamic Model Results, Fright 41 (concluded)

NASA/CR--2002-211485 25

A

<


12000 ........ _........ F....... _ ....... J ....... _ ....... ._._._._:, ......... _ ......

8000 .... ,........ ,........ , ............ __ ...... _'_-"_ ....... , ...... ---.-_- ....

J , , I , I i I '_.

P

0_- ................... i i, ................ i i Ti .......... i '1/L I L J I / 0

0 1000 2000 3000 4000 5000 6000 7000 8000

FuelPress

72 I ] I I _ i

i I I i //

-_'_. ...................... j______/_ ....70 ........... _ .... '

68

3

66

64

62

] i I i

i I I 1

I I I i I I

0 1000 2000 3000 4000 5000 6000 7000 8000

OilPress

75 I I I ', I i /I I _ I I

70 ........ *........ *...... _ ....... "--_ .... -_ ....... _- .... .,_ _ L__ _ _;_ _-_ , . _.-. _ ,, -._1 _ , ;

60 ....... _........ ,-

_ I I I

55 _ _ I _ L _ I I0 1000 2000 3000 4000 5000 6000 7000 8000

Fuel Temp

310

300F ................ _---_ ....... _ ....... _-...... L ......o

290 ...... _........ ,........ r ..... r -

measured data ' _ ' ' "

t_" 280_- ___ Right44model -- --_ ....... _ ....... -_-- . - , .....

l Rights41&44rr_lel ] I : i : :I I I270 " I i I I

0 1000 2000 3000 4000 5000 6000 7000 8000

time (sec) RVlDS Right44

Figure 3-9. Linear Dynamic Model Results, Flight 44

NASA/CR--2002-211485 26

1050

2

_1000

950

I _ I I I

....., .....i-F1_----i.......i.....i....i.....'_......

....-_-_,

_ i I I I0 1000 2000 3000 4000 5000 6000 7000

EGT3

1050

8000

1000,

$_- 950

9O00

1040,

_. 1020 !o.. I¢ 1000!

. 980

96o

9400

; I F [ I

I r F I; _ I J

I \v I

I I

1000 2000 3000 4000 5000 6000 7000 8000

EG T5

i"_ % I ] [ [ I

....... , ...... _-D-_-.C-'_.-j,--_-_ _ ...... _ ..... "-,-.7 .... ,,, ,

....... ,........ ,:_,___- -_,d__-____..__,:,.___:.... __,_:____)_--::\,j -.,-.,.-.__T- -, ........

,_h,,,4mo,_, l_'-'_-- :-"-;---: ...........Rights41&44 model / " " "

I J I I I I

1000 2000 3000 4000 5000 6000 7000 8000



NAS A/CR--2002-211485 27

EGT61020 , f _ _

_-. 1000 - :...... : -_c - -i ............. ', - - T ....... _-.....

980 ....... ........ i ....... _......... - .... _-.... _-I , , _/,,%_v_v/ I ' , , .7 I

, , , , ,96o _............... ..... ....940 ........... :......... -_ ....920 / ; l J I i

ov

D

0 1000 2000 3000 4000 5000 6000 7000 8000

CHT1

480 | F_ !,,6oL....... _........ .--%...... .--.-/--__\I....... _---_-_-_...... _-.......

38o I : , i i i .... i

o

7

J \_/ I

0 1000 2000 3000 4000 5000 6000 7000 8000

CHT3

50o ! _ i ! !I r I I i r

I I _ I I

4501 ....... '........ _---... - -, ........ r ....... r- ......

I i i \ I I I

I \ I I

400 ........ ............... , __ _ =- - - - _ ...... \_ .... r _ - -_- -

350

5O0

I I I I _ /i

I I I I ,

J I I I I

3OOO

450

_)

_- 4o0

0 1000 2000 4000 5000 6000 7000 8000

CHT4

3500

I 1 ; I II J i I , I

I _ I I I I

........ I............ \ -I ........ _ - - T ....... F .......

I \\ I

measured data ] ' " __'- _ _ _ - _"• i I I I % \ I/Right44 model , , , , -.

-- Rights41 &44 model I ' ,' ,' "" - ' ,I l i , I ; I

1000 2000 3000 4000 5000 6000 7000 8000

time (sec) RV[_ Right44


NASA/CR--2002-211485 28

400

CHT5

I I I [ Ii I I I i

I I I I

I I I I I

_,} _l_U ...... Pb....... t% _-E-- - _ -i ....... :_'_J_*_ i i J7-- r_ ....... F .....

_4.rl I J I

0 1000 2000 3000 4000 5000 6000 7000 8000

CHT6

4601 I 1 I

I I I

I I I I r

420, ..... ,.... ,- - - :, "_ - _- - ._'_,"- -_, _ - ...... • - -_ - -

__ I I ", 4 / x ) I ]I I _ D_ If_ _i_ / _ _ ___.J ..... C .........400 ............ i.... ,-/ _-" ---'_

I I P _ t X I I /380 .............. ,..... _....... _..... _ .... ----....... ;"......

9,_n I I L I J

0 1000 2000 3000 4000 5000 6000 7000 8000

ManifoldAirPr

100 I l ] l I l lI I I I r I

II I _ P I

-- _ . , _._ _ _ , . ,...... i ...... I_ - - ._I .J_I_ ..... J ....... .L ....... ..- ..... L .... L----

_u ........ _ ........ I........ -2---_Dcr ....... T ...... _ - ---

i i i i _

if)

¢_ 70 ........ ,...... ,....... -_....... _ ....... _-_ -,_'_- - 7. - '- .......

an i i i i

0 1000 2000 3000 4000 5000 6000 7000 8000

EngineRPM

r i ]

...................... _j .... J ........ L ...............

........ II........ I_I .... II.............

measured data t -_ -2 _;; ..... f_ ...... : ..... I' ',. - -Flight44 model _,- [ , ,.

• .to------.. model _ , , , , , .,I i I I L I I

28O0

" 2600

_24O0

o9

•_ 2200

2000'0 1000 2000 3000 4000 5000 6000 7000 8000


Figure 3-9. Linear Dynamic Model Results, Flight 44 (concluded)

NASA/CR--2002-211485 29

3.6 Nonlinear Dynamic Modeling

In reviewing the flight test data, it was observed that there existed a nonlinear relationship

between the engine RPM and the SLPC setting. As the SLPC setting was set to values just below

0.6, there was little change in engine RPM. Presumably in this region, the FADEC is adjusting

the propeller pitch to satisfy the pilot's power level command. We were unable to acquire

technical information about the FADEC control laws to verify this assumption. However, this

measured characteristic of the SLPC and engine RPM response provides an opportunity to create

a piecewise linear model consisting of two operating regions:

• Region 1: SLPC values greater than or equal to 0.6

• Region 2: SLPC values less than 0.6

3.6.1 Nonlinear Dynamic Modeling Approach

The nonlinear dynamic model is constructed in state-space form. The form of the model is asfollows:

k = Ax + Bu + b * min(0, SLPC - 0.6)

y = Cx + Du

where

x = state vector

u = input vector

y = output vector

A,B,C,D = linear system matrices for Region 1 (SLPC > 0.6)

b -- vector of coefficients used to adjust the response for Region 2 (SLPC < 0.6)

The nonlinear model was developed using the following procedure:

1. Using measured data from Region 1, system matrices A, B, C, and D were computed

using the method developed for the earlier linear dynamic models.

2. Using measured data from Region 2 and system matrices A, B, C, and D, the b vector was

computed using a least-squares approximation.

3.6.2 Model Data Preparation

The nonlinear dynamic models were developed using the data collected in the two flight tests.

The flight test data files prepared earlier for the linear modeling were reused for this work.

Region 1 and Region 2 of each flight were determined based on SLPC setting, as shown in

Figure 3-10.

NASA/CR--2002-211485 30

10000

= 5000

<

300O

" 2800

"_ 2600

_ 2400

2200

20000

0.8

.c_

0.6

_ 0.4_J

0.2

Flight 41 Altitude

> _6 ' _ _ \SLF'C O ....... i ........ _ .

rr , rr/ 1, ,rl 1, , ,,-_ ....4" _ _,'_ ,

I: II/ :II i: I I\It

/J', ,, ,',, ,: , , ,................;" I; II :11 1; II I0

0 2000 4000 6000 8000

Flight 41 Engine Speed

I! i [ I I I 11 I I I

, t.} ......... /, I[_,II _! I, II I !:r,t

_._. : _...........I,_Ti ,I! _T-ll "1-j

I: li iii Ii II Ii2000 4000 6000 8000

Flight 41 SLPC Setting

1

_--I_- L $ -_* * _ - L-_ J,--L --4

,_ _ i,l_ tl, |/ /

_ _L__:___a _____a.____a__

/ : tJ ; : l |0

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i i

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it

Figure 3-10. Nonlinear Dynamic Model Data

3.6.3 Nonlinear Dynamic Model Development

Three nonlinear dynamic models were developed using the technical approach described above.These models are as follows:

• NL Model 41 created from Flight 41 test data

• NL Model 44 created from Flight 44 test data

• NL Model 4144 created from a combination of Flight 41 and Flight 44 test data

The flight test data used in developing these models is shown in Table 3-4. Since the partitioning

of the flight test data into two regions resulted in fewer data points in each region, we were

forced to reduce the number of inputs and state variables in order to achieve models that were

stable.

NASA/CR--2002-211485 31

Table 3-4. Nonlinear Model Data

Inputs States Not Used Bad Sensors

Altitude

Altitude Slope

Outside Air Temp

SLPC

Cowl Flaps

(Dummy Input)

Oil Pressure

Oil Temperature

EGT3

CHT3

Engine RPM

Kollsman

Bay 2 Temperature

Fuel Pressure

Fuel Temperature

EGT2

EGT5

EGT6

CHT1

CHT4

CHT5

CHT6

Manifold Air Pressure

Air Charge Temp

Pressure Altitude

Bay 1 Temperature

EGT1

EGT4

CHT2

3.6.4 Nonlinear Modeling Results

The above nonlinear models were used to simulate a flight using initial conditions taken from

either Flight 41 or Flight 44. Simulated results were compared with actual measured flight test

data. The results are shown in Figures 3-11 and 3-12. The plots in Figures 3-11 and 3-12 were

prepared as described in Table 3-5.

Table 3-5. Nonlinear Model Plots

Figure Flight Models Used Plotted Data

3-11 41 41 and 4144 Measured data: heavy line

Nonlinear Model 41 data: dashed

Nonlinear Model 4144 data: solid

3-12 44 44 and 4144 Measured data: heavy line

Nonlinear Model 44 data: dashed


NASA/CR--2002-211485 32


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NASA/CR--2002-211485 33

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Figure 3-11. Nonlinear Dynamic Model Results, Flight 41 (concluded)

NASA/CR--2002-211485 34

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NASA/CR--2002-211485 35

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3.6.5 Comparison of Nonlinear and Linear Modeling Results

A simulation was performed to compare the quality of the linear and nonlinear modeling

methods. The nonlinear model 4144 compared to a linear model 4144 using the same inputs and

states (as listed in Table 3-4). The results are shown in Figures 3-13 and 3-14. The plots inFigures 3-13 and 3-14 were prepared as described in Table 3-6.

Table 3-6 Comparison Plots

Figure Flight Models Used Plotted Data

3-13 41

3-14 44

Nonlinear model

vs. Linear model

(modeled for

combined Flights41 and 44)

same as above



Nonlinear Model 41 44 data: solid




NASA/CR--2002-211485 36

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NASA/CR--2002-211485 37

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NASA/CR--2002-211485 38

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NASA/CR--2002-211485 39

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Figure 3-14. Nonlinear vs. Linear Model Results, Flight 44 (concluded)

3.7 Data Analysis Conclusions

Conclusions drawn from the analysis of the flight test data and the resulting static correlation

models and dynamic models are presented in the following subsections.

3.7.1 Static Correlation Modeling Conclusions

Analysis of the static correlation modeling results provided the following observations:

• Disconnected sensors are clearly detectable using a static correlation model that compared

sensor output values with expected values (see Figure 3-6).

• Intermittent sensor faults are also clearly detectable using a static correlation model (see

Figure 3-2).

• Data from multiple flights can be combined to produce an improved static correlation

model (see Figures 3-3, 3-4, and 3-5).

• Static correlation models worked well for detecting sensor faults.

NASA/CR--2002-211485 40

3.7.2 Dynamic Modeling Conclusions

Analysis of the dynamic modeling results provided the following observations:

Combining data from both test flights generally produced better models. This can be seen

for the linear models in the last subplot in Figure 3-9 (comparing model 4144 and model

44).

• Nonlinear models were better than linear models for the same number of inputs and states.

This can be seen by comparing the nonlinear and linear model results in the last subplot in

Figure 3-13. The nonlinear models do, however, require more empirical data to generate.

• As expected, more data gives a better model. This can be seen by comparing the linear

model in the last subplot of Figure 3-8 with that in the last subplot of Figure 3-13.

• Extending the dynamic model results from these two flight tests to future development

(having many more flight tests) will produce much improved dynamic models. Combining

the empirical modeling approach with a physics-based modeling approach also has the

potential for improved accuracy.

• The two flight tests performed on this program demonstrate that dynamic models of the

engine/aircraft can be produced using relatively simple and inexpensive instrumentation

such as would be found in a commercializable on-board engine diagnostic system.

• Dynamic modeling has the potential to detect mechanical faults internal to the engine aswell as sensor faults.

NASA/CR--2002-211485 41

Section 4. Technology Roadmap

The PMDS concept shows promise as a means to improve the pilot's awareness of the condition

of the engine. This technology will require more development before it can be commercialized

and broadly applied in the aviation marketplace. Honeywell has prepared an overview of the key

technology areas that require further development. This information is presented for the purpose

of guiding the direction of future development. The key technology development areas are

• Modeling and fault diagnosis algorithms development

• Hardware and software development

• System development

The following subsections discuss these three key areas and provide a roadmap for future

development of each area.

4.1 Modeling and Fault Diagnosis Algorithms Development

The PMDS concept employs model-based diagnostic technology. Mathematical models for the

engine, sensors, and related equipment are created from first-principles analysis and from

empirical data collected from flight and ground-based testing. These models are used to detect

faults in the engine, sensors, and related equipment. Fault information is used to make failure

diagnoses. This process (for empirical models) is shown in Figure 4-1.

Make Empirical / Physics-basedPlant Model

ModelIdentification

Plant ModelSimulationModel

U_ Use Plant Model for FaultDetection and Isolation

input plant Modelvariables _JSimulation

I _ IModel

Data variables

at

prediction_ residual

IFault Estimation

-_]Algorithms

fault estimates

Figure 4-1. Modeling and Fault Detection

NASA/CR--2002-211485 42

4.1.1 Background

Modeling-System modeling for engines can be done in various ways. Empirical modeling uses

input/output data to derive a model of the engine. First-principles modeling uses physics and

thermodynamics, etc. to derive a model of the engine. The pros and cons of each approach, as

well as that of a combined approach, are discussed below.

The advantage of empirical models is that they can be very simple and general enough to apply

to a variety of systems with little change from system to system. The disadvantage of empirical

models is that they typically require many unknown coefficients that need to be identified using

extensive empirical data. A general rule, based on the Cramer-Rao bounds, is that the number of

required sample points is proportional to the square of the number of unknown coefficients.

Given enough sensors and sample points, it is possible to evaluate all the coefficients, but if any

system dynamics change, it may be necessary to start over. For example, given a system with

x _ R", numerically differentiated state derivatives _ R" and u _ R m , coefficient matrices

A _ R ''×'', B _ R ''_' , and a linear empirical model of the form .4"= [A, B]*[x; u], the unknown

coefficient matrices [A,B] can be evaluated using k samples of each of the n+m measurements of

x and u. Let

F = [_(tl ) ..... )?(tk )] _ R ''×k

x(tl) ..... x(t k)] R _'+''_×k= E

G [u(t l), ,u(t_)

Then F = [A,B]*G and [A,B] = F*G'*inv(G*G').

Since the n+m coefficients in each row of [A,B] only affect the derivative of the corresponding

state, those n+m parameters need to have k > (n+m)^2 samples of that state derivative in order to

satisfy the quadratic Cramer-Rao bounds. With n = 5 and m = 5, that requires k > (5+5)^2 = 100.

With n = l0 and m = 5, we would need k > (10+5)^2 = 225 samples. With the test flights used in

this study, we had 110 good test points in each of the two flights, for a total of k -- 220. This

means that a single flight, with 1 l0 samples, is enough to identify a five-state model, while a ten-

state model would require the data from both flights together.

The advantages of a first-principles model include the ability to do "what if" experiments with

the model and the ability to adapt the model to new untested situations. Another advantage of

first-principles models is that they typically have fewer parameters than empirical models. It is

possible to take advantage of the structure imposed by multiple time scale data and the natural

separation of the system dynamics to create multiple subsystem models.

The parameters in a first-principles model are closely related to things that can be measured in

isolation from the rest of the system. This makes it possible to combine the general structure of

the first-principles model with empirical data to calibrate a few unknown parameter values. The

disadvantage of first-principles models is that they take considerable time to derive and must be

tailored to each specific application.

In our application, the only nonempirical information that we used was the nature of the

nonlinearity in the SLPC. The SLPC input controls a combination of RPM and propeller pitch.

The system behaves essentially like one linear system above SLPC = 0.6, and like another linear

NASA/CR--2002-211485 43

system when SLPC is at lower levels. Rather than doubling the number of parameters, we kept

the same A matrix and simply added one column to the B matrix, which multiplied the added

input: rain(0, SLPC - 0.6).

Failure Detection and Fault Diagnosis-During the AGATE program, a set of failure detection

and fault diagnosis algorithms was developed. A set of engine models is used in computing an

estimate of each of the various engine performance parameters needed for fault diagnosis. An

example failure detection algorithm for oil pressure is shown in Figure 4-2.

Oil L__ Filtering, A/D _P°f _0_[Pressure [ /] and unit cony.

[Sens°rI I ,Olo;+tOo lO]II i-ll Tol&anceI

RPM _ I------ _ Detection

Oil Pressure IOil Temp"----_ Estimate

Oil Quant-----_

APop

Periodic sampling

at a period of tm

Average

Average

Store Instantaneous

) out-of-tolerance

value

>

J Store APopave

Issue

Maint.

-x tol Warning

Early Wamin I

Tolerance &

Detection

>

Store Popave

Figure 4-2. Oil Pressure Failure Detection and Warning Diagnosis

The anticipated failures and the associated detection for each of the engine subsystems are

arranged in a matrix of probable faults with the associated detection methods. An example fault

diagnosis matrix related to oil pressure is shown in Table 4-1.

NASA/CR--2002-211485 44

Table 4-1. Oil Pressure Fault Modes and Corresponding Detection Tests

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The current fault detection amounts to noting when a measured output deviates (by more than

some threshold amount, for several samples) from the value of the model output, when the model

is being fed the same inputs as the actual system. The current fault diagnosis assumes that the

fault lies with the sensor whose value is deviating from the model prediction. A more detailed

fault diagnosis would have to include engine faults as well as sensor faults.

An empirical way to calibrate an engine fault model would be to record sensed variables with

and without each expected fault. Another option would be to derive a first-principles model of

how each fault affects each sensor output. It would likely be necessary to combine the methods,

using a first-principles model and using empirical data to calibrate the remaining unknown

coefficients associated with the faults.

4.1.2 Future Development

Professor Giorgio Rizzoni and Gary L. Parker at Ohio State University have developed a first-

principles individual-cylinder model of an internal-combustion engine under the AGATE

program. This model was implemented in Simulink. In future applications of our PMDS

technology, we would like to use our existing flight data to evaluate some of the coefficients in

such a first-principles model. For example, an empirical modification is often needed for the

first-principles model of the relationship between throttle setting and manifold pressure. We

NASA/CR--2002-211485 45

would also like to get empirical data for the most common types of faults experienced by general

aviation engines.

A PMDS also would benefit greatly from additional inputs from outside the engine, such as

aircraft speed, aileron, elevator, rudder, and flap settings, propeller pitch, etc. With a

combination of a first-principles model and the availability of sufficient measurements of engine

and aircraft variables, it will be much easier to achieve a good calibration of the modelcoefficients.

Future development of an engine monitoring and diagnostic system will require a prioritized list

of engine faults to guide the diagnostic development work. Ideally, this technical information

should come from an engine manufacturer. Data from more than one engine manufacturer would

be even more helpful in advancing the technology.

While at present most general aviation operators are using gasoline-powered piston engines,

current development programs in alternative-fuel engines, such as diesel engines, can offer a

means to collect the engine fault information needed for the eventual development of an engine

monitoring and diagnostic capability for these new engine technologies.

The application of vibration monitoring is an obvious area of interest in the subject of engine

monitoring and diagnostics. Certain engine conditions such as bearing wear are best detected

through vibration monitoring. The AGATE PMDS hardware was designed to optionally take

information from a vibration sensor mounted directly on the engine, process the vibration data,

and determine prognostics from that data. A university-led study performed under the Honeywell

AGATE program has shown that, for piston engines, vibration monitoring can be used to detect

engine conditions such as bearing wear. However, the team discovered that it was not sufficient

to analyze the frequency spectrum of the vibration data as in traditional vibration monitoring

methods, but rather to use a direct sample of the vibration time signal. This preliminary study

used 5000 samples/second for one second of engine operation in order to detect engine

conditions sufficient for prognostic prediction. Further research and development will be

necessary before such optional vibration data can be used in a reliable fashion for engine

prognostics.

The future development steps described above are shown in Figure 4-3. At this time, the scope of

the work, the timing, the source of development funding, and the makeup of the development

team are undefined. These planning issues will be addressed as the general aviation community

continues to dialog about engine diagnostics technology and market needs.

NASA/CR---2002-211485 46

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47

4.2 Hardware and Software Development

During the AGATE program, Honeywell developed a prototype design for the PMDS. This hardware

and software design will require additional development to bring it to a commercializable form. The

following subsections describe the key hardware and software development areas ahead.

4.2.1 Background

The AGATE prototype system architecture concept is shown in Figure 4-4.

Bus Interface

Airframe

Displays andWarnings

Power

Supplies

Avionics Bus

Bus Interface ThrottleController

PropellerController

FADEC

IgnitionN,knifle

Engine

Single LeverPower Control

Propell_Govemor

Pilot

Figure 4-4. PMDS Architecture

PMDS Hardware Design-The PMDS hardware architecture is shown in Figure 4-5. (The hardware

arrangement used in our flight testing under this program and in the earlier AGATE flight testing did not

use individual engine sensors, but relied on a serial communication interface to the FADEC. The test

setup is described in Section 3 of this report.) The present PMDS hardware is a prototype design

implemented under the AGATE program. This PMDS hardware is shown in Figure 4-6.

NASA/CR--2002-211485 48

During the design of the PMDS hardware in the AGATE program, Honeywell solicited guidance from

the AGATE community about electronic design standards. At the time, the AGATE electronic design

standards were still evolving, so the Honeywell design team opted to design the prototype PMDS

hardware using then-available best practices for guidance (i.e., for lightning, EMI, thermal, shock, and

other design criteria). The resulting PMDS hardware has performed flawlessly in all flight testing to

date.

Engine

Sensors

FADECController

Sensors

I ll

I t, I

III

Serial Ilnt_face I

II I

tI t, I

IIIIII

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Memory

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andYagr_

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'//'///_

"/////_

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DiagnosticOutput

_:i_:_ DisplaySerialInterface

PMDS

Figure 4-5. PMDS Hardware Architecture

PMDS Software Design-The present PMDS software was implemented under the AGATE program and

consists of the following key elements:

• Executive program with supporting modules

• Communications to a PC for data transfer

• Communications to the FADEC for sensor data collection

• Data conversion and storage

• Built-in-test and power-up sequencing

NASA/CR--2002-211485 49

The present PMDS software resides in two places: the run-time portion that runs on the PMDS

hardware, and a retrieval portion that runs on a PC and receives saved data from the PMDS at PMDS

startup.

The PMDS software runs on "bare metal," i.e., without an operating system, as a single-thread

application. At power-on, it initializes the hardware, conducts startup tests, and then sends any

previously stored data out the maintenance port. A PC connected to the maintenance port and ready to

receive the data will have the data sent over in a "raw" form, but be able to convert this data to a moreusable form.

Figure 4-6. PMDS Hardware

After startup, the universal asynchronous receiver/transmitter (UART) for the port connected to the

FADEC is assigned an interrupt handler and enabled, and the main thread goes into an endless loop.

FADEC data is sent in records. As each byte of FADEC data arrives over the port, the interrupt handler

builds the record that is being sent. When a record is complete, the main loop is given access to the

record. Statistical information is computed for each field in the record and saved for later computations.

With each record, a counter is incremented; when the counter reaches a threshold, the statistical

information is written to the EEPROM for long-term storage.

The stored data is sent over the maintenance port whenever the PMDS powers up. To download the

data, a PC must be attached via serial cable to the PMDS and the PC-side software must be running,

waiting for the data to be transmitted. The data is sent in "raw" format; the PC software converts it to ahuman-readable format.

NASA/CR--2002-211485 50


The PMDS hardware was originally designed several years ago under conditions that are markedly

different than today. At that time, the hardware price goal drove designers to go with a dual-processor

design using relatively low-power processors and forego expensive dual-port RAM for communication

between the processors in favor of a more complicated approach with cheaper memory. These

approaches and the lack of commercial alternatives led to a platform that would be difficult to upgrade

in the future as processor capabilities and memory capacity increase while prices decrease.

In the time since those design decisions were made, the state of the art for small, embedded processors

and cards has changed substantially. In addition to the costs of processors and memory going down and

the power and capacity of processors and memory going up (Moore's Law), there are many more off-

the-shelf components available. Processors substantially more powerful than the present PMDS's

Motorola ColdFire 5206 are available, such as the Intel Pentium (II/III/IV) series, the PowerPC series,

and MIPS and ARM. Many different single-board computers are commercially available in the PC-104

format (the same card format used in the PMDS). Also interesting is the increasing number of system-

on-a-chip systems, which integrate a higher degree of functionality on a single chip, replacing functions

that would otherwise be found on the system board. All of these developments can allow a more

capable system for less investment. More important, they can provide the same processing capability in

one processor as two ColdFire processors. Not only is this a simpler hardware design, but it also

simplifies the software. One factor, however, must be kept in mind: candidate processors and boards

must be chosen to meet applicable environmental requirements for temperature, humidity, and vibration.

Faster, more common processors with more memory will allow more flexibility with regard to software,

both in the size of the applications and in their sophistication. The PMDS used a single-threaded

application with interrupt service routines that receive data from the FADEC. This approach was

dictated by the limited memory on the PMDS and the desire to keep things simple. Adding more

complex algorithms to the PMDS may require a more capable infrastructure, such as an operating

system (OS) might provide. Options range from open-source OSs such as eCOS

(http://sources.redhat.com/ecos/) and Linux to commercial OSs such as Windows CE, VxWorks, and

LynxOS. By using these OSs, standardized programming interfaces and off-the-shelf tools can be used.

Advancements in digital avionics will also benefit the PMDS concept. It was noted in Section 4.1 that

the PMDS would benefit greatly from additional inputs outside the engine, such as aircraft speed,

aileron, elevator, rudder, and flap settings, propeller pitch, etc. These other sources of data (outside the

FADEC) could provide information to the PMDS over a digital avionics bus in the aircraft. This data-

sharing capability will enable the use of more sophisticated models (through more sensory information)

and will help to minimize the installed cost of the PMDS. The ability for different systems on board the

aircraft to share digital data is a key enabler for the development and commercialization of the PMDS

concept.

The future development steps described above are shown in Figure 4-7. At this time, the scope of the

work, the timing, the source of development funding, and the makeup of the development team are

undefined. These planning issues will be addressed as the general aviation community continues to

dialog about engine diagnostics technology and market needs.

NASA/CR----2002-211485 51

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4.3 System Development

During the AGATE program, Honeywell developed a prototype system design for the PMDS concept.

This system design will require additional development to bring it to a commercializable form. The

following subsections describe the key system development areas ahead.

4.3.1 Background

The current PMDS technology embodies the early set of system requirements defined during the

AGATE program. We expect these system requirements to evolve as the development continues. Some

of the key system requirements that will guide future PMDS development are

• Applications

• Certification issues

• Pilot interface

• Ground technician interface

These topics are briefly described below.

Applications-The PMDS flight testing performed to date has been done using the Aurora Flight

Sciences Chiron aircraft with the Teledyne IO-360ES engine. Thus, all of our results are specific to that

engine. Future development must, of course, address other engine models and other engine

manufacturers. As market studies are made, a target market of key engine applications will be identified.

These applications will be the focus of the next steps in the system development process

Certif'wation Issues-The PMDS is intended strictly as a monitoring system and diagnostic-aiding

system. The PMDS shall not provide test inputs to the propulsion system in order to perform. Rather, it

will only monitor the propulsion system. Its outputs in flight will be limited to engine failure and

warning indications. All decisions as to required pilot actions are strictly the purview of the pilot. All

maintenance decisions are strictly the purview of the ground maintenance technician. The PMDS is not

intended to replace these judgments. In view of these considerations, certification of the PMDS need

only be to nonessential levels. The communication interface with the FADEC (and any other avionic

equipment for collecting input data) will be one-way only. In this sense, the PMDS is isolated and

cannot interfere with the operation of other critical systems on the aircraft. More definition of system

requirements with respect to certification will be made as the development continues.

Pilot Interface-The PMDS shall provide two elements to the pilot interface: an engine failure indication

and an engine warning indication. An engine failure indication means that the PMDS has detected an

engine failure that may immediately affect the engine's power output or cause immediate harm to the

engine. An engine warning indication means that the PMDS has detected an impending failure and the

pilot should initiate a maintenance action before the next flight. More definition of pilot interface

requirements will be made as the development continues.

Ground Technician Interface-During ground maintenance, the PMDS shall have a data interface for

ground maintenance technicians, allowing them to interrogate the PMDS and determine fault and

impending fault indications. This capability will enable technicians to download the diagnostic

information that was used by the PMDS to determine the fault or warning indication. This shall include

a fault history and information on the signals that caused the indications to be made. This information is

NASA/CR--2002-211485 53

limited to fault andearlydetectioninformationandis not intendedto performasaflight datarecorder.Theinterfaceshallpresenttimesof failureandearlydetections,valuesof therelevantsignals,detailsofwhichtestscausedtheindications,andanindicationof thepossiblefault modesthatled to theindication,all in aform thatgroundmaintenancepersonnelcanreadilyuse.


The above system requirements will be incorporated into future development work. Honeywell

envisions that the next stage of development could be accomplished using two approaches: testing with

a pending failure and fleet testing.

Testing with a Pending Failure-This type of testing would be accomplished as either:

Limited flight tests, for failures that are not safety risks, such as failures of noncritical sensors or

equipment, and/or

Ground tests, for failures that pose a safety risk. This would also provide a means to achieve

greater breadth and depth of testing.

Fleet Testing-This type of testing would consist of long-term flight testing with a fleet of aircraft to

diagnose a specific class of failures (i.e., those that can be detected and diagnosed using data from

EGTs, CHTs, and/or other recorded PMDS parameters). This type of testing will enable the

development team to examine a wide range and long duration of actual operating conditions.

These two approaches could be used independently or in parallel, depending on the makeup of the

development team. Future development steps using these approaches are shown in Figure 4-8. At this

time, the scope of the work, the timing, the source of development funding, and the makeup of the

development team are undefined. These planning issues will be addressed as the general aviation

community continues to dialog about engine diagnostics technology and market needs.

NASA/CR--2002-211485 54

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Section 5. New Technology

Per the requirements of the NASA contract, this section is intended to "identify all nonpatentable

discoveries such as improvements, innovations, and computer codes; and all patentable

inventions, whether developed or discovered during performance of this contract. Possible

secondary applications of reported new technology should also be included in this section."

In performing the technical work on this program, the Honeywell team did not create any new

technology. As described throughout this report, our work on this program was primarily an

extension of work that was begun on the AGATE program. This program's primary results are

the completion of AGATE flight testing and the demonstration of the viability of PMDS

technology. As such, there were no discoveries that fit any of the descriptions in the paragraph

above. (During the AGATE program, Honeywell did prepare an invention disclosure covering

various facets of the PMDS concept. Honeywell is currently pursuing patent protection for that

intellectual property.)

NASA/CR--2002-211485 56

Section 6. Conclusions and Recommendations

This NASA program has taken the results of Honeywell's AGATE work and completed the

initial evaluation of the capability of the prototype PMDS hardware. This work provided the

following key results:

It demonstrated the ability of the PMDS to detect a class of selected sensor hardware

failures. Disconnected sensors were clearly detectable using a static correlation model that

compared sensor output values with expected values. Intermittent sensor faults were also

clearly detectable using a static correlation model.

It demonstrated the ability of the PMDS hardware to successfully model the engine for the

purpose of engine diagnosis. The two flight tests performed on this program demonstrated

that dynamic models of the engine/aircraft can be produced using relatively simple,

inexpensive instrumentation such as would be found in a commercializable on-board

engine diagnostic system. Not surprisingly, nonlinear dynamic models performed better

than linear dynamic models for the same number of inputs and states. Also as expected,

the greater the number of test data points, the better the quality of the resulting model. (A

full-scale development project would involve many sets of flight test data, thereby

resulting in improved dynamic models.) Dynamic models could be used to detect faults

internal to the engine as well as sensor faults.

Future development work for an engine monitoring and diagnostic system should employ the

following elements:

• Engine/aircraft modeling should combine first-principles and empirical approaches. This

strategy offers the advantage of using fewer parameters as required by first-principles

models, while using empirical methods to calibrate unknown parameter values as needed.

• The monitoring and diagnostic system should employ additional inputs outside the engine,

such as aircraft speed, aileron, elevator, rudder, and flap settings, propeller pitch, etc. This

strategy will result in an improved dynamic model to be used for fault detection.

• A prioritized list of engine faults is needed to guide the diagnostic development work.

Ideally, this technical information should come from an engine manufacturer. Data from

more than one engine manufacturer would be even more helpful in advancing the

technology.

• The monitoring and diagnostic system should be able to gather input data from the

FADEC and other systems in the aircraft over a digital avionics bus. This data-sharing

capability will enable the use of more sophisticated models (through more sensory

information) and will help to minimize the installed cost of the PMDS.

NASA/CR--2002-211485 57

Appendix

The Appendix contains the following items:

• List of PMDS sensors

• Flight 41 documentation (consisting of Flight Test Plan 8F34, and Flight Test Engineer's

Report 8F35)

• Flight 44 documentation (consisting of Flight Test Plan 8F35, and Flight Test Engineer's

Report 8F36)

NASA/CR--2002-211485 59

PMDS Sensor Inputs

Ch. Data Ch.

1 labels

2 Altitude intercept 38

3 Altitude slope 39

4 Altitude min 40

5 Altitude max 41

6 Kollsman intercept 42

7 Kollsman slope 43

,8 Kollsman min 44

9 Kollsman max 45

10 FuelPress intercept 46

11 FuelPress slope 47

12 FuelPress min 48

13 FuelPress max 49

14 OilPress intercept 50

15 OilPress slope 5116 OilPress min 52

17 OilPress max 53

18 FuelTemp intercept 54

19 FuelTemp slope 55

20 FuelTemp min 56

21 FuelTemp max 57

22 OilTemp intercept 58

23 OilTemp slope 59

24 OilTemp min 60

25 OilTemp max 61

26 BaylTemp intercept 62

27 Bay1Temp slope 63

28 Bay1Temp min 64

29 BaylTemp max 65

30 Bay2Temp intercept 66

31 Bay2Temp slope 67

32 Bay2Temp min 68

33 Bay2Temp max 69

34 EGT1 intercept 70

35 EGT1 slope 7136 EGT1 min 72

37 EGT1 max 73

Data

EGT2 intercept

EGT2 slope

EGT2 min

EGT2 max

EGT3 intercept

EGT3 slopeEGT3 min

EGT3 max

EGT4 intercept

EGT4 slopeEGT4 min

EGT4 max

EGT5 intercept

EGT5 slope

EGT5 min

EGT5 max

EGT6 intercept

EGT6 slopeEGT6 min

EGT6 max

CHT1 intercept

CHT1 slopeCHT1 rain

CHT1 max

CHT2 intercept

CHT2 slopeCHT2 min

CHT2 max

CHT3 intercept

CHT3 slopeCHT3 min

CHT3 max

CHT4 intercept

CHT4 slope

CHT4 min

CHT4 max

Ch. Data

74 CHT5 intercept

75 CHT5 slope

76 CHT5 min

77 CHT5 max

78 CHT6 intercept

79 CHT6 slope80 CHT6 min

81 CHT6 max

82 OutsideAirTemp intercept

83 OutsideAirTemp slope

84 OutsideAirTemp min

85 OutsideAirTemp max

86 ManifoldAirPr intercept

87 ManifoldAirPr slope88 ManifoldAirPr min

89 ManifoldAirPr max

90 AirChargeT intercept

91 AirChargeT slope

92 AirChargeT min

93 AirChargeT max

94 EngineRPM intercept

95 EngineRPM slope

!96 EngineRPM min

97 EngineRPM max

98 PressureAItitude* intercept

99 PressureAItitude* slope100 PressureAItitude* min

101 PressureAItitude* max

* PressureAItitude = mbar/10

Flight Test Data Collected Manually

Ch. Data

102 SLPC

103 Cowl flaps

NASA/CR--2002-211485 60

SLPC CHIRON AU-008

[AGATE Integrated Flight Test Plan AGATE 8F34

N427AU

Operations Number: 8F34Date: 04/02/01

Estimated Flight Time: 2.5 Hours

Range Time: 5:00 to 7:00Proposed Engine Start: 4:45

Flight Crew: Pilot in Command:

Flight Test Engineer:

Bill Weber

Ken Zugel

Test Objectives: flight with PMDS, SLPC-FADEC to 4,000 ft; 8,000 fl and 12,000 ft. Several single-lever powersettings. Sensors all nominal.

Test Event Summary:Taxi.

Takeoff in SLPC mode.Set Power = 100%.

Climb to 4,000 feetSet Power = 40% cruise.

Set Power = 60% cruise.Set Power = 80% cruise.

Flight - see Test Card 8F33

Reach steady state.

Reach steady state.Reach steady state.

Set Power = 100% climb to 8,000 ft.Set Power = 40% cruise. Reach steady state.

Set Power = 60% cruise. Reach steady state.Set Power = 80% cruise. Reach steady state.

Set Power = 100% climb to 12,000 ft.

Set Power = 40% cruise. Reach steady state.

Set Power = 60% cruise. Reach steady state.Set Power = 80% cruise. Reach steady state.

Pull power back to 40%, descend to 4,000 ft.Set Power = 75% for enroute climb to 8,000 ft.

Level off at 8,000 fl at Power = 75%.Continue enroute climb to 12,000 ft.

Descend to airport.

Conduct approach.Landing.Debrief.

Call Signs: Aircraft Call Sign:

Frequencies: Manassas TowerAdditional Information:WX:

Support Equipment: N/A

Aircraft Configuration:

N427AU

SLPC - FADEC active, FTC inactive.

NASA/CR--2002-211485 61

FCU Software:

ECU Software:Fuel:

Operating Limits:

Special Precautions:

MAF sensor OPERATIVE, Tbay sensor OPERATIVE

(SLPC system). Honeywell PMDS active.

CH8.10

Full Main Tanks

Conditions: wet/dry, daylight, VFRMax. altitude: 12,500 feet

Secure Ramp Area at Aurora Hangar.

r.. ]

SLPC CHIRON AU-008 IN427AUlIAGATE Integrated Flight Test Plan AGATE 8F34 I

SIGNATURES FOR FLIGHT APPROVAL:

Quality Assurance:

Director of Engineering:

Aurora FRR Board Chairman:

Director of Flight Ops:

Project:

Date:

Date:

Date:

Date:

Date:

NASA/CR--2002-211485 62

IPMDS Flight Test 8F34 TEST CARD SLPC CHIRON AU-008 I

IN427AUI FLIGHT DATE:

Event: Test Description:

1. CHECKLIST PROCEDURE AND START FRONT ENGINE IN SLPC MODE.

2. START AND CHECK SLPC DATA LOG AS PER PROCEDURE.

3. START PMDS DATA LOG AS PER PROCEDURE.

4. WARMUP.

5. TAKEOFF. POWER = 100%.

6. CLIMB TO 4,000 FEET.

7. ADJUST COWL FLAPS AS NECESSARY, MAKE NOTES.

8. SET 40% POWER. REACH STEADY STATE.

9. SET 60% POWER. STEADY STATE.


11. SET 100% POWER, CLIMB TO 8,000 ft.

04/12/01








19. SET 40% POWER OR MIN., DESCEND TO 4,000 ft.

20. SET 75% POWER, LEVEL FLIGHT.

21. INITIATE ENROUTE CLIMB TO 8,000 ft.

22. LEVEL OFF, CONSTANT 75% POWER.

23. INITIATE ENROUTE CLIMB TO 12,000 ft.

24. DESCEND TO AIRPORT.

25. APPROACH.

26. INITIATE LANDING PATTERN, LAND.

27. SWITCH OFF SLPC DATA LOG PRIOR TO FADEC SHUTDOWN.

28. SHUTDOWN.

NASA/CR--2002-211485 63

Flight Test Engineer Report

SLPC/PMDS Flight Test 8F35 (ref: Flight Test Plan "AGATE 8F34" 04/02/01)25 April 2001Bill Weber- Pilot

Ken Zugel - Flight Test Engineer

Narrative:

This was a repeat of the previous flight after replacing the propeller governor.

Bill and I manned the aircraft at 1215 and started the engines at 1230. We completed the checklist and poweredup SLPC/PMDS system. The Manassas weather was clear, 10 miles visibility, winds were from 360 at 10 knots,and the altimeter setting was 30.28"hg. Following the runup and system checks, we took off at 1245 in SLPC

mode and departed toward the southwest. We experienced problems with the landing gear system during the

climbout. The gear doors did not close after the gear retracted. The gear warning horn activated during the first

gear up cycle and the gear warning circuit breaker popped. Bill recycled the gear and it retracted normally andthen he reset the breaker. Once the gear problems were resolved Bill continued the departure and configured the

aircraft for cruise climb between 20 and 65% power. He made slow climb to 4000 ft due to airspace and air trafficcontrol limitations. The test area was roughly between Culpeper, Charlottesville, and New Market due to airtraffic concerns.

Upon reaching 4000 ft we proceeded with the cruise test points. We reversed the order of the test points from the

previous flight. The altitude varied +/- 100 ft. We experienced light to moderate turbulence. The 80% power testpoint began at 1300 and was nominal. The cowl flaps were closed once the engine temperatures stabilized. The

60% test point began at 1306 and was nominal as well. The 40% power setting began at 1313 and was nominal aswell.

The climb to 8000 ft was performed at 90% power vice the 95% on the test card due to engine/propeller

limitations. The cowl flaps were opened for the climb. We leveled off at 8000 ft and started the 80% power pointat 1325. The cowl flaps were closed after the temperatures stabilized. The 60% power test point was started at

1331 and was nominal. The 40% power setting was started at 1338. We did not experience any of the propellerRPM oscillations that we did on the previous flight.

The climb to 12000 ft was performed at 100% power and the climb rate varied between 300 and 500 feet perminute. Once we reached altitude and the engine temperature stabilized, the cowl flaps were closed and we started

the 80% power test point at 1353. The 60% power test point was started at 1359 and was nominal. The 40%power test point was started at 1405 and was also nominal.

From the last test point Bill initiated the descent to 8000 ft at 40% power. We leveled off at 8000 ft for the 75%

power test point and it was successfully completed at 1424. We continued the descent to 4000 ft at 40% power.Bill leveled off at 6000 ft to clear terrain and then continued the descent to 4000 ft once we were east of the

mountains. He continued the descent at 40% power until we entered the pattern. We returned to Manassas andconfigured for landing. Bill made a normal landing at 1451. We taxied in and shutdown the aircraft at 1457.

Conclusions:

The SLPC system worked better than the last flight with the new propeller governor.

Recommendations:

Proceed with the next SLPC/PMDS test flight Friday at 1200.

NASA/CR--2002-211485 64

SLPC CHIRON AU-008 N427AU[ AGATE Integrated Flight Test Plan AGATE 8F35

Proposed Engine Start: 1200Date: 04/25/01

Range Time: 1230 to 1430Estimated Flight Time: 2.5 Hours

Flight Crew: Pilot in Command:

Flight Test Engineer:

Bill Weber

Ken Zugel

Test Objectives: PMDS, SLPC-FADEC to 4,000 fl: 8,000 fl and 12,000 ft. Several single-lever power settings.

Test Card:

Power up and start data logFADEC Start and Taxi.

Runup and test SLPCTakeoff in SLPC mode.Set Power = 100%

Reduce to 95% leaving pattern

Climb to 4,000 feet

Set Power = 80% cruise. Reach steady state. Time for 5 minutesSet Power = 60% cruise. Reach steady state. ""

Set Power = 40% cruise. Reach steady state. ""

Set PowerSet Power

Set PowerSet Power

= 95% climb to 8,000 ft.

= 80% cruise. Reach steady state. "

= 60% cruise. Reach steady state. '"'= 40% cruise. Reach steady state. ""

Set PowerSet Power

Set PowerSet Power

= 100% climb to 12,000 ft.

= 80% cruise. Reach steady state. '"'

= 60% cruise. Reach steady state. '"'= 40% cruise. Reach steady state. '"'

Descend to 8,000 ft. at 40%,

Level off at 8,000 ft at 75%. Reach steady state. Time for 5 minutes.Reduce power to 40%, continue descent to pattern altitudeDescend to airportConduct approach and Go-Around (if needed) in SLPC mode

LandingStop data log and shutdownDebrief.

Call Signs: Aircraft Call Sign: N427AUFrequencies: Manassas Tower: 133.1

Mission at 1400: 123.45

Aircraft Configuration: SLPC - FADEC active, FTC inactive.MAF sensor INOP, Tbay sensor INOP.

(SLPC system). Honeywell PMDS active.FCU Software:ECU Software: CH8.10

Fuel: Full Main Tanks, Full Aux. Tanks

Operating Limits:

Special Precautions:

Conditions: wet/dry, daylight, VFRMax. altitude: 12,500 feet

Secure Ramp Area at Aurora Hangar.

NASA/CR--2002-211485 65

SIGNATURES FOR FLIGHT APPROVAL:

Quality Assurance:

Director of Engineering:

Aurora FRR Board Chairman:

Director of Flight Ops:

Project:

Date:

Date:

Date:

Date:

Date:

NASA/CR--2002-211485 66

IPMDS Flight Test 8F35 TEST CARD SLPC CHIRON AU-008 J

lN427AU FLIGHT DATE: 04/25/01

Event: Test Description:

29. CHECKLIST PROCEDURE AND START FRONT ENGINE IN SLPC MODE.

30. START AND CHECK SLPC DATA LOG AS PER PROCEDURE,

31. START PMDS DATA LOG AS PER PROCEDURE.

32. RUNUP/SWITCH TO FADEC MODE

33. TAKEOFF in SLPC POWER = 100%.

34. CLIMB TO 4,000 FEET.

35, ADJUST COWL FLAPS AS NECESSARY, MAKE NOTES.

36. SET 80% POWER, REACH STEADY STATE.







43, SET 100% POWER, CLIMB TO 12,000 ft.




47. SET 40% POWER, DESCEND TO 8,000 ft,

48. SET 75% POWER, LEVEL FLIGHT.

49. LEVEL OFF, CONSTANT 75% POWER.

50. SET 40% POWER, DESCEND TO 4000 ft.

51. APPROACH AND GO-AROUND IN SLPC MODE,

52. INITIATE LANDING PATIERN, LAND,

53. SWITCH OFF SLPC DATA LOG PRIOR TO SHUTDOWN.

54.SHUTDOWN.

NASA/CR--2002-211485 67

Flight Test Engineer Report

SLPC/PMDS Flight Test 8F36 (ref: Flight Test Plan "AGATE 8F35" 04/25/01)27 April 2001Bill Weber- Pilot

Ken Zugel - Flight Test Engineer

Narrative:

This flight was a repeat of the previous flight with the T-bay temperature and Mass Airflow sensors disconnected.

The Manassas weather was 9000ft overcast, 10 miles visibility, winds were from 300 at 7 knots, the temperaturewas 21 degrees Celsius, and the altimeter setting was 30.08"Hg.

Bill and I manned the aircraft at 1345 and started the engines at 1401. We completed the checklist and poweredup SLPC/PMDS system. Following the runup and system checks, we took off at 1416 in SLPC mode and

departed toward the southwest. We didn't have any problems with the landing gear system during the climbout.Bill pulled the gear warning circuit breaker to prevent a false alarm and keep the gear down bulb from burning out

as had occurred on the previous flight. Bill continued the departure and configured the aircraft for cruise climbbetween 75% power. He made a slow climb to 4000 ft due to airspace and air traffic control limitations. The testarea was roughly between Culpeper, Charlottesville, and Fredericksburg due to ceilings.

Upon reaching 4000 fl we proceeded with the cruise test points. The altitude varied +/- 200 ft. We experienced

continuous light to moderate turbulence. The cowl flaps were kept open due to the higher outside air

temperatures. The 80% power test point began at 1426 and was nominal. The 60% test point began at 1432 andwas nominal. The 40% power setting began at 1438 and was nominal as well.

The climb to 8000 fl was performed at 90% power due to engine/propeller limitations and resulted in a 500 fpmrate of climb. The cowl flaps were open for the climb and the entire 8000 ft test block due to the higher OAT. We

leveled off at 8000 ft and started the 80% power point at 1451. The manifold pressure (MAP) stabilized at 22" Hgand the RPM was at 2450. The 60% power test point was started at 1457 and was nominal. The MAP was 21" andthe RPM was 2350. The 40% power setting was started at 1503 and was nominal as well. The MAP was 20.75"and the RPM stabilized at 2325.

The climb to 12000 fl was performed at 100% power and the climb rate varied between 300 and 500 feet perminute. The indicated power command was only 96%, but appears to have been caused by a change in the Single

Power Lever position sensor. We encountered wake turbulence from a B-727 descending through our altitude,which triggered several warnings and caused two momentary upsets. Once we reached altitude and the engine

temperature stabilized, the cowl flaps were closed and we started the 80% power test point at 1520. The MAP was19" and the RPM stabilized at 2525. The 60% power test point was started at 1526 and was nominal. The 40%

power test point was started at 1533 and was also nominal.

From the last test point Bill initiated the descent to 8000 ft at 40% power. We leveled off at 8000 fl for the 75%

power test point at 1545. Once it was completed we continued the descent at 40% power until we entered thepattern. The rate of descent was between 500 and 1000 fpm due to airspace and traffic limitations. The weather at

Manassas had changed: the altimeter setting was 30.00"Hg, the skies were clear, and the temperature was 25

degrees Celsius. We returned to Manassas and made a normal landing at 1601. We taxied in and shutdown theaircraft at 1607.

Conclusions:

The SLPC system functioned nominally.

The Single Power Lever position sensor should be recalibrated if additional SLPC flights are planned.

Recommendations:

The erroneous gear warnings and malfunctions seem to be switch related and may need to be fixed prior to the

next flight test.Proceed with the next SLPC/PMDS test flight if requested and the schedule will allow it.

NASA/CR--2002-211485 68

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I 1 AGENCY USE ONLY (Leave b/ank) 2. REPORT DATE 3. REPORT TYPE AND DATES COVERED

March 2002 Final Contractor Report5. FUNDING NUMBERS4. TITLE AND SUBTITLE

Flight Test of Propulsion Monitoring and Diagnostic System

6. AUTHOR(S)


7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

Honeywell Laboratories

3660 Technology Drive

Minneapolis, Minnesota 55418

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

WU-728-30-20-00

NAS3-01105

8. PERFORMING ORGANIZATIONREPORT NUMBER

E-13253

10. SPONSORING/MONITORINGAGENCY REPORT NUMBER

NASA CR--2002-211485

11. SUPPLEMENTARY NOTES

Pr_ectManager, Donald L. Simon, VehicleTechnology Directorate, NASA Glenn Research Center, organization

code 0300,216---433-3740.

12a. DISTRIBUTION/AVAILABILITY STATEMENT

Unclassified - Unlimited

Subject Category: 07 Distribution: Nonstandard

Available electronically at httt)://eltrs._rc.nz_sa._ov/GLTRS

This publication is available from the NASA Center for AeroSpace Information, 301--621-0390.

12b. DISTRIBUTION CODE

13. ABSTRACT (Maximum 200 words)

The objective of this program was to perform flight tests of the propulsion monitoring and diagnostic system (PMDS)

technology concept developed by Honeywell under the NASA Advanced General Aviation Transport Experiment

(AGATE) program. The PMDS concept is intended to independently monitor the performance of the engine, providing

continuous status to the pilot along with warnings if necessary as well as making the data available to ground maintenance

personnel via a special interface. These flight tests were intended to demonstrate the ability of the PMDS concept to detect

a class of selected sensor hardware failures, and the ability to successfully model the engine for the purpose of engine

diagnosis.

14. SUBJECT TERMS

Aircraft engines; General aviation aircraft; Systems health monitoring; Flight safety;

Modeling; Diagnostics

17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION

OF REPORT OF THIS PAGE OF ABSTRACT

Unclassified Unclassified Unclassified

15. NUMBER OF PAGES

7516. PRICE CODE

20. LIMITATION OF ABSTRACT

NSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89)

Prescribed by ANSi Std. Z39-1B298-102

Documents

Flight Test of Propulsion Monitoring and Diagnostic System · This report presents the results of the NASA program entitled "Flight Test of Propulsion Monitoring and Diagnostic System,"