Automation & Robotics Research Institute (ARRI) The ...Automation & Robotics Research Institute (ARRI) The University of Texas at Arlington. Automation & Robotics Research Institute

F.L. Lewis, Assoc. Director for ResearchMoncrief-O’Donnell Endowed Chair

Head, Controls, Sensors, MEMS GroupAutomation & Robotics Research Institute (ARRI)

The University of Texas at Arlington

Automation & Robotics Research Institute (ARRI)The University of Texas at Arlington

F.L. Lewis, Assoc. Director for ResearchMoncrief-O’Donnell Endowed Chair

Head, Controls, Sensors, MEMS Group

http://ARRI.uta.edu/acs

CBM II - Diagnostics

ObjectivesExtend equipment lifetimeReduce down timeKeep throughput and due dates on trackUse minimum of maintenance personnelMaximum uptime for minimum effective maintenance costsCBM should be transparent to the user

No extra maintenance for the CBM network!Determine the best time to do maintenance

Efficiently use maintenance & repair resourcesDo not interfere with machine usage requirements

Allow planning for maintenance costsNo unexpected last-minute costs!

Condition-Based Maintenance (CBM)Prognostics & Health Management (PHM)

The CBM/PHM Cycle

MachineSensors

Pre-Processing

FeatureExtraction

FaultClassification

Predictionof Fault

EvolutionData

ScheduleRequired

Maintenance

Systems &Signal processing

Diagnostics Prognostics MaintenanceScheduling

Identify importantfeatures

Fault Mode Analysis

Machine legacy failure data

Available resourcesRULMission due dates

Required Background Studies

Contact George [email protected]://icsl.marc.gatech.edu

PHMCBM

Off Line- Background Studies, Fault Mode AnalysisOn Line- Perform real-time Fault Monitoring & Diagnosis

Two Phases of CBM Diagnostics

Three Stages of CBM/PHM

DiagnosticsPrognosticsMaintenance Scheduling

CBM – Fault Diagnosis Background Studies

• Fault Mode Analysis (FMA) - Identify Failure and Fault Modes

• Identify the best Features to track for effective diagnosis

• Identify measured sensor outputs needed to compute the features

• Build Fault Pattern Library

Deal with FAULTSNeed to identify Faults before they become Failures

Phase I- Preliminary Off Line Studies

Compressor Pre-rotation Vane

Condenser

Evaporator

•Compressor Stall & Surge•Shaft Seal Leakage•Oil Level High/Low•Aux. Pump Fail•Oil Cooler Fail•PRV/VGD Mechanical Failure

•Condenser Tube Fouling•Condenser Water Control Valve Failure•Tube Leakage•Decreased Sea Water Flow

•Target Flow Meter Failure•Decreased Chilled Water Flow•Evaporator Tube Freezing

•Non Condensable Gas in Refrigerant•Contaminated Refrigerant•Refrigerant Charge High•Refrigerant Charge Low

•SW in/out temp.•SW flow•Cond. press.•Cond. PD press.•Cond. liquid out temp.

•Comp. suct. press./temp.•Comp. disch. press./temp.•Comp. oil press./flow (at required points)•Comp. bearing oil temp•Comp. suct. super-heat•Shaft seal interface temp.•PRV Position

•Liquid line temp.•(Refrigerant weight)

•CW in/out temp./flow•Eva. temp./press.•Eva. PD press.

Ex. Ex. -- Navy Centrifugal Chiller Failure ModesNavy Centrifugal Chiller Failure Modes

Fault Mode Analysis Contact George [email protected]://icsl.marc.gatech.edu

Fault Modes of an Electro-Hydraulic Flight Actuator

V. Skormin, 1994SUNY Binghamton

bearingcontrol surface

hydrauliccylinder

pump

poweramplifier

Fault Modes

Control surface lossExcessive bearing friction

Hydraulic system leakageAir in hydraulic systemExcessive cylinder frictionMalfunction of pump control valve

Rotor mechanical damageMotor magnetism loss

motor

Fault Mode Analysis

Use Physics of Failure and Failure Models to select failure features to include in feature vectors

Select Fault ID Feature Vector

Method 1- Dynamical System Diagnostic Models

The Fault Feature Vector is a sufficient statistic for identifying existing fault modes and conditions

BJssTs

+=

1)()(ωmotor dynamics

sBsMsFsX

pp )(1

)()(

+=pump/piston dynamics

LsKAsR

sP

+=

)(

1)()(

2actuator system dynamics

Physical parameters are J, B, Mp, Bp, K, L


Select Feature VectorRelate physical parameters J, B, Mp, Bp, K, L to fault modes

Get expert opinion (from manufacturer or from user group)Get actual fault/failure legacy data from recorded machine historiesOr run system testbed under induced faults

Result -

Etc.Etc.

THEN (fault is air in hydraulic system)IF (actuator stiffness K is small)AND (piston damping coeff. Bp is small)

THEN (fault is excess cylinder friction)IF (motor damping coeff. B is large)AND (piston damping coeff. Bp is large)

THEN (fault is hydraulic system leakage)IF (leakage coeff. L is large)Fault ModeCondition

Therefore, select the physical parameters as the feature vectorT

pp LKBMBJt ][)( =φ


Select Sensors for the Best Outputs to Measure


Tpp LKBMBJt ][)( =φ

Cannot directly measure the feature vector

Can measure the inputs and outputs of the dynamical blocks, e.g.

BJssTs

+=

1)()(ω

)(2

)()( tPDtCItTπ

−= ω(t)motor speed

armaturecurrent I(t)

pressuredifference P(t)

Therefore, use system identification techniques to estimate the features

Virtual Sensors = physical sensors + signal processing se

nsor

sDSP

signals from machine

Fault IDfeatures

Method 2- Non-Model-Based Techniques


Etc.Etc.

THEN (fault is worn outer ball bearing)IF (third harmonic of shaft speed is present)AND (kurtosis of load vibration is large)

THEN (fault is gear tooth wear)IF (shaft vibration second mode is large)AND (motor vibration RMS value is large)

THEN (fault is unbalance)IF (base mount vibration energy is large)Fault ModeCondition

Therefore, include vibration moments and frequencies in the feature vector

=)(tφ [ time signals … frequency signals ]T

Get expert opinion (from manufacturer or from user group)Get actual fault/failure legacy data from recorded machine historiesOr run system testbed under induced faults

Method 3- Statistical Regression Techniques


Vibration magnitude

Driv

e tra

in g

ear t

ooth

wea

r

Pearson’s correlationNonlinear correlation techniquesMultivariable regression

Clustering techniquesNeural networksStatistical

Fault 1

Fault 2

Fault 3

outliers

Etc.Etc.

THEN (fault is air in hydraulic system)IF (actuator stiffness K is small)AND (piston damping coeff. Bp is small)

THEN (fault is excess cylinder friction)IF (motor damping coeff. B is large)AND (piston damping coeff. Bp is large)

THEN (fault is hydraulic system leakage)IF (leakage coeff. L is large)Fault ModeCondition

Fault Pattern Library

Etc.Etc.

THEN (fault is worn outer ball bearing)IF (third harmonic of shaft speed is present)AND (kurtosis of load vibration is large)

THEN (fault is gear tooth wear)IF (shaft vibration second mode is large)AND (motor vibration RMS value is large)

THEN (fault is unbalance)IF (base mount vibration energy is large)Fault ModeCondition

Fuzzy Logic Rulebase !

www.MIMOSA.orgMachine User Group- CBM Data

Condition Monitoring and Diagnostics of Machines

CBM Fault DIAGNOSTICS Procedure

machines

Math models

),,(),,(

ππ

uxhyuxfx

==&

System Identification-Kalman filterNN system ID

RLS, LSE

Dig. Signal Processing

PhysicalParameterestimates &Aero. coeff.estimates

π̂

Sensoroutputs

VibrationMoments, FFT

FeatureVectors-

Sufficientstatistics

)(tφFault ClassificationFeature patterns for faultsDecision fusion could use:

Fuzzy LogicExpert SystemsNN classifier

Stored Legacy Failure dataStatistics analysis

Feature extraction -determine inputs for Fault Classification

Physics of failureSystem dynamicsPhysical params.

Identify Faults/Failures

Set Decision ThresholdsManuf. variability dataUsage variabilityMission historyMinimize Pr{false alarm}Baseline perf. requirements

More info needed?

Inject probe test signals for refined diagnosisInformpilotyes

π

Serious?

Informpilot

yes

SensingFault Feature Extraction

Reasoning& Diagnosis

Systems, DSP& Data Fusion

SensorFusion

Featurevectors

Featurefusion

StoredFault Pattern

Library

Model-BasedDiagnosis

Phase II- On Line Fault Monitoring and Diagnostics

no

Request Maintenance

Fault Classification

Decision-MakingFault Classification

StoredFault Pattern

Library

Feature Vectors

)(tφ

Diagnosed Faults

Neural networksFuzzy logicExpert system rulebaseBayesianDempster-Shafer

Model-Based Reasoning (MBR) vs. Case-Based Reasoning

Too complex!Faults often depend on Operating conditions

Fuzzy Logic Fault ClassificationUnifies

expert systemsstatisticalneural network approaches

2-D FL system c.f. neural network

Fig 1 FL rulebase to diagnose broken bars in motor drives usingsideband components of vibration signature FFT [Filippetti 2000].

Number of broken bars = none, one, two.Incip. = incipient fault

small medium large

smal

lm

ediu

mla

rge

Sideband component I1

Side

band

com

pone

nt I 2

none incip.

incip.

one

one

one

oneortwo

oneortwo

two

... ..

.........

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

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

.. ..

.

. ..

Fig 5 Clustering of statistical fault data

Vibration magnitude

Driv

e tra

in g

ear t

ooth

wea

r

Faul

t con

ditio

ns

one

two

thre

e

low med severe

FL Decision Thresholds

From Chestnut

Based onLegacy fault data historiesManuf. variability dataUsage variabilityMission historyMinimize Pr{false alarm}Baseline perf. requirements

Can be tuned using adaptive NN learning techniques

Mode DiagramExample Testbed: AC-Plant


Possible failures depend on current operating mode

Model-Based ReasoningMBR

Joint Strike Fighter –Prognostics and Health Management (PHM)

‘Joint Strike Fighter’,JSF, and the JSF Logo are Trademarks of the United States Government

Public Released Under: AER200307014

Michael Gandy and Kevin LineLockheed Martin Aeronautics

PHM Architecture and Enabling Technologies

Air Vehicle On-BoardHealth Assessment

Health Management,Reporting & Recording

Autonomic Logistics& Off-Board PHM

PVI

MAINTAINERVEHICLE INTERFACE

Mission Critical

PHMData

Displays & ControlsCrashRecorder

MaintenanceInterface Functions

IETMsConsumables

On-Board Diagnostics

PMD

.

PMA

In-Flight &Maintenance Data Link

Flight Critical

PHM / Service Info

Database

AMD/PMD

PHM Area Managers

MS Subsystems

• Sensor Fusion• Model-Based Reasoning• Tailored Algorithms• Systems Specific

Logic / Rules• Feature Extraction

Provides:

• AV-Level Info Management• Intelligent FI• Prognostics/Trends• Auto. LogisticsEnabling/Interface

Methods Used:

FCS/UtilitySubsystems

NVMICAWSManager

AVPHMHosted in ICP

Structures

MissionSystems

• Decision Support• Troubleshooting and Repair• Condition-Based Maintenance• Efficient Logistics

Vehicle Systems

Propulsion

Results In:

ALIS• Automated Pilot / Maint. Debrief

• Off-Board Prognostics

• Intelligent Help Environment

• Store / Distribute PHM Information

Hosted in ICP

Michael Gandy and Kevin LineLockheed Martin Aeronautics

Model Legend -Model Legend -Condition Function

SensorComponent

BlockDiagram

MBRModel

MBR Approach Provides Multiple Benefits and Functions:– Intuitive, Multi-Level Modeling– Inherent Cross Checking for False Alarm Mitigation– Multi-Level Correlation for Failure Isolation Advantage

Chains of Functions Indicate Functional Flows.– Components Link to the Functions They Support.– Sensors Link to the Functions They Monitor.– Conditions Link to the Functions They Control.

Michael Gandy and Kevin LineLockheed Martin AeronauticsModel-Based Reasoning (MBR) Provides a

Significant Part of PHM Design Solution

PEDS Software System PEDS Software System Architecture Architecture

(Stand(Stand--alonealone))

5. Feature Extraction

5. Feature Extraction4. Feature

Extraction4. Feature Extraction

Case-basedDiagnostic Reasoner

Case-basedDiagnostic Reasoner

Hardware•Plant•Sensors•DAQ

Hardware•Plant•Sensors•DAQ

3. Mode Estimator /Usage PatternIdentification

3. Mode Estimator /Usage PatternIdentification

Central DBEvent DispatchEvent Dispatch

Database ManagementDatabase

Management

CBMmain. schedule FAHPFAHP

Causal Adjustments

Causalfactors

ScenarioGenerator

ScenarioGenerator

DWNN

VirtualSensor(WNN)

CPNN

failuredimension

6.

Classifier(WNN)

Classifier(WNN)

Classifier(Fuzzy)

Classifier(Fuzzy)

5.

2. Data Preprocessing

2. Data Preprocessing

1. GUI1. GUI


Documents

Automation & Robotics Research Institute (ARRI) The ...Automation & Robotics Research Institute (ARRI) The University of Texas at Arlington. Automation & Robotics Research Institute