Prognostics of Aircraft Bleed Valves Using

a SVM Classification Algorithm

Renato de Pádua MoreiraCairo L. Nascimento Jr.

Instituto Tecnológico de AeronáuticaInstituto Tecnológico de AeronáuticaSão José dos Campos - BrazilSão José dos Campos - Brazil

2

Summary

Objectives SVM The Method Case Study Implementation Conclusions

3

Objectives

There are many PHM methods, but few use classification algorithms.

Capacity of SVM classifier could be applied to PHM. Both flight data parameters and maintenance logs can

be used as inputs for the classification. The classification result would be an input for a

degradation index to indicate the unit’s health.

Support Vector Machines

Supervisioned learning method based on the statistical learning theory (Vapnik)

Used for: Classification; Pattern Recognition; Regression;

Mainly Applied on: Bioinformatics; Text classification; Image Recognition;

4

Support Vector Machines

5

Support Vector Machines

Classical Constrained Quadratic Optimization Problem Use of Lagrange method (1797), extended by Khun-Tucker (1951)

The problem becomes (dual form):

Maximize

Subject to

6

Non-linearly separable universe

Mapping in the Feature Space

Use of Kernel Functions:

Support Vector Machines

7

K(x,xi) = φT(x) φ (xi)

The Method

8

Tra

inin

gG

ener

aliz

atio

n

The Method

1. Training the Classifier 2. Generalizing for new flights

9

Extraction of Characteristics and

date/time for each flight

Extraction of significant maintenance actions

SVM TrainingAnd

Tuning

{(xi,yi)}

Preparation of Training Datasetwith the adopted definition of

HEALTHY and UNHEALTHY

SVM Structure

Flight Data Maintenance Logs

Calculation of Degradation Index

New Maintenance

Logs

SVMClassification

Extraction of Characteristics and date/

time for each flight

gi

di

SVM Structure

NewFlight Data

Case Study: Aircraft Bleed Valve

Why the Bleed Valve Unit?

Component of the AMS (Air Management System) that controls the cabin temperature, pressurization, air renewing and cycling,

Critical for aircraft dispatchability (AOG),

Low MTBF (Mean Time Between Failures),

Just one maintenance action is allowed: replace the unit,

Availability of Flight Data (hours) and Maintenance Data (replacement logs).

10

Case Study: Aircraft Bleed Valve

11

Implementation

1. Collection of data: Flight data: Manif. Press., Manif. Temp., N2 (high pressure

compressor speed) Maintenance Logs: Left Bleed Valve Replacements (date/time)

12

0 1000 2000 3000 4000 5000 6000 7000 80000

1

2

3

4x 10

4

Alti

tud

e

0 1000 2000 3000 4000 5000 6000 7000 80000

25

50

75

100

N2

0 1000 2000 3000 4000 5000 6000 7000 80000

20

40

60

80

Ma

nif.

Pre

ssu

re

Flight Time (seconds)0 1000 2000 3000 4000 5000 6000 7000 8000

50

100

150

200

250

Ma

nif.

Te

mp

era

ture

Flight i Windowing the flightAt least 20 minutes of stable cruise

Extraction of 8 CharacteristicsTime Domain: mean, standard

deviation, skewness, kurtosis, median

Freq. Domain: RMS power, peak and power over a 0.002 Hz

No. of inputs = 3 x 8 = 24

Implementation

13

0 0.1 0.2 0.3 0.4 0.5 0.6 0.70

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

x1

x2

UNHEALTHY

HEALTHYSupport Vectors

Optimum Decision Surface

Example with 2 parameters

Implementation

1. Generalization for new flights

Observation: The rate of UNHEALTHY seems to increase up to a replacement

14

200 300 400 500 600 700 800-1

0

1

Cla

ssifi

catio

n

ReplacementsH

EA

LTH

YU

NH

EA

LTH

Y

Time (days)

Implementation

1. Degradation Index Problem: Create an index from 0 to 1, taking into account:

Classification Results (rate of UNHEALTHY) Noise (different flight profiles may cause misclassification) Gaps in the data collection

Solution: Calculation of UNHEALTHY rate in a time window (W = 30), containing a

variable quantity of flights (depending on the data availability), or

15200 300 400 500 600 700 8000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time (days)

Deg

rada

tion

Inde

x

Results

Aircraft A Aircraft B

16

200 300 400 500 600 700 8000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time (days)

Deg

rada

tion

Inde

x

250 300 350 400 450 500 550 600 650 700 750 8000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time (days)

Degr

adat

ion

Inde

x

Only data from AIRCRAFT A was used to train the SVM.

Conclusions The method uses a SVM classification algorithm trained with a

dataset collected during several flights. Maintenance logs are used to compute the label of each data and to

“reset” the degradation index. The trained classifier can be applied to every new flight of any

aircraft of the same model (generalization to other aircrafts). The method does not require a deep knowledge of the unit. It does not require either the fault pattern or health trend to be

visually identifiable. Failures happening too close would not be detected. Different failure modes would not be distinguished, unless the

classifier is trained separately.

17