3-optimal sensor selection (simon)

7/30/2019 3-Optimal Sensor Selection (Simon)

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Aviation Safety ProgramAviation Safety Program IVHM ProjectIVHM Project

Optimal Sensor Placement for

Propulsion Gas Path Diagnostics

Donald L. Simon

George Kopasakis

T. Shane Sowers


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Outline

Sensor Placement Background / Motivation

Systematic Sensor Selection Strategy (S4)

Methodology Overview

Turbofan Engine Application Example

Discussion and Summary


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Optimal Sensor Placement Motivation

Aircraft engine gas path

diagnostics

Based upon parameter

interrelationships within the gas

turbine cycle

Enabled by digital engine controls

and available control sensor

measurements

Consists of engine performance

trend monitoring, event detection

and fault isolation

Fault types exceed number of

available sensor measurements

A holistic system-level approach to

sensor selection is desired

Physical

Problems

Degraded

Module

Performance

Changes in

Measurable

Parameters

Result in Producing

Permitting

correction of

Allowing

isolation of

Gas Turbine Cycle Parameter Inter-relationships


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Systematic Sensor Selection Strategy (S4)

Methodology Overview

Background: Developed under NASA Space IVHMefforts

Provides a systematic evaluation ofthe available sensor suite relative tothe diagnostic requirements

Selects sensors (type/location) to

optimize the fidelity and response ofengine health diagnostics

Architecture Functionality: Knowledge Base:

System simulation Health information

Down-select process: Diagnostic model Merit function Down-select algorithm

Statistical evaluation: Considers sensor response and

system/signal noisecharacteristics

Knowledge Base

Knowledge Base

Iterative Down -Select Process

Iterative Down -Select Process Final Selection

Final Selection

Health

Related

Information

Health

Related

Information

Down -Select

Algorithm

(Genetic Algorithm)

Down -Select

Algorithm

(Genetic Algorithm)

System

Diagnostic

Model

System

Diagnostic

Model

Sensor Suite

Merit

Algorithm

Sensor Suite

Merit

Algorithm

Optimal

Sensor

Suite

Optimal

Sensor

Suite

Candidate SensorSuitesCandidate Selection

Complete

YesNo YesNo

Collection of NearlyOptimal

SensorSuites

Application Specific Non-application specificApplication SpecificApplication Specific Non-application specificNon-application specific

Statistical

Evaluation

Algorithm

Statistical

Evaluation

AlgorithmSystem

Diagnostic

Model

System

DiagnosticModel

Sensor Suite

Merit

Algorithm

Sensor Suite

MeritAlgorithm

System

Simulation

System

Simulation


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S4 Methodology: Turbofan Engine ApplicationExample


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S4: Turbofan Engine Application Example

High-Bypass Commercial Turbofan Engine Simulation

Non-linear aero-thermodynamic component levelmodel

5 Rotating Components (FAN, LPC, HPC, HPT,LPT)

Candidate Sensors:

Baseline Sensors: N1, N2, T25, T3, Ps3, T49, Wf36

Optional COTS Sensors: P17, T17, P25, T5, P5

Gas Path Faults Considered

10 single parameter engine faults (!Fan ,"Fan , !LPC

,"LPC , !HPC ,"HPC , !HPT ,"HPT , !LPT ,"LPT )

Cruise Operating Point


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Diagnostic Module

Processes normalized residualmeasurements

Fault Detection monitors root-mean-squared value of thenormalized residual measurements

i

baselineimeasuredi

i

yyy

!

__

"=#

Example Gas Path Fault Signature

m = number of sensor measurements

( )!=

"=m

i

iym

d1

21


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Diagnostic Module (cont.)

Fault Discrimination (Isolation): Applies inverse model approach. Faulthypothesis which produces smallest residual estimation error is inferred to bethe fault condition.

Potential risk of mis-classifying these two faults

iii yyyerrorestimationresidual

~ !"!=!

estimatesdelmoInversey

tsmeasuremeny

i

i

=!

=!


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Sensor Suite Merit Function

( )!=

"=m

i

ikj ym

D1

21 ~,

Residual measurement agreement metricbetween actual fault kand hypothesized

faultj:

Fault discrimination metric for fault k:

kjfora

kjfora

faultsofnumbern

where

aDZn

j

kjk

=!=

"=

=

=#=

1

1

1

:

,

Sensor suite merit value:

weightingkfaultW

penaltysuitesensorP

faultsofnumbern

where

ZWPMerit

k

n

k

kk

=

=

=

= !=

:

1

Residual measurement agreement

metric, Dj,k, values for faults 1-10 (5%)

Fan ! and LPT ! faults at risk

for mis-classificationLPC " fault at risk for

missed detection


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Statistical Evaluation Algorithm

A final statistical evaluation is performed to evaluate and rank the top performingsensor suites:

Adds sensor noise

Other system uncertainty factors

Residual measurement agreement metric ( Dj,k )

Baseline senor suite (blue) vs.

Optimal sensor suite (red) Comparison

Baseline 7

Sensor Suite

1. N1

2. N2

3. T25

4. T3

5. Ps3

6. T497. Wf36

S4 Optimal 10

Sensor Suite

1. N1

2. N2

3. T25

4. T3

5. Ps3

6. T497. Wf36

8. P17

9. P25

10. T5


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Monte Carlo Simulation Confusion Matrix Results (2.5% to 5.0%

Faults)

100%100000000000000No Fault

100%099900000000110

69%0069000000003109

100%000100000000008

100%000010000000007

84%13800008430019006

100%00002099800005

13%84800126021260604

63%34304002500627013

100%000000000100002

67%0132500000006741

AccuracyNo Fault10987654321

100%100000000000000No Fault

100%0100000000000010

100%00997000000039

100%000100000000008

100%00009990100007

95%2210009540023006

100%00003099700005

100%00010009990004

88%5319004070881093

100%000000000100002

97%003500000009651


TrueF

aultCondition

True

FaultCondition

Inferred Fault Condition


7 Baseline

Sensors

13% miss-detect rate

7% mis-classify rate

10 Optimal

Sensors

0.8% miss-detect rate

1.3% mis-classify rate

> 5% mis-classif ications or missed detections 0.1% to 5% mis-classif ications or missed detections


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Summary

An initial application of the Systematic Sensor Selection

Strategy (S4) has been applied to a turbofan engine application

Demonstrated improved diagnostic performance with selected

optimal sensor suite

Follow-on efforts will apply updated performance metrics and

additional system functionality (fault types, operating scenarios,

advanced sensors)

Provides a systematic approach towards the evaluation and

selection of candidate sensors and diagnostic algorithms


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Backup Slides


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Monte Carlo Simulation Confusion Matrix Results (2.5% to 5.0%

Faults)

100%100000000000000No Fault

100%0100000000000010

100%00997000000039

100%000100000000008

100%00009990100007

95%2210009540023006

100%00003099700005

100%00010009990004

88%5319004070881093

100%000000000100002

97%003500000009651


TrueF

aultCondition

True

FaultCondit

ion



10 Sub-

Optimal

Sensors(adds P17, T17,

P5)



10 Optimal

Sensors



> 5% mis-classif ications or missed detections 0.1% to 5% mis-classif ications or missed detections

100%100000000000000No Fault

100%099610000000310

94%009440000000569

100%000100000000008

100%000010000000007

95%2900009480023006

100%00004099600005

62%24900362321861805404

90%5447003110896073

100%000000000100002

89%0111200000008871



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FADEC

Control Sensors

T12

P0

T25 T3

PS3WF N1 N2 T45

Optional

Sensors

PS13

T13

P25T0

P2

Advanced

Sensors

Combustor

Fan

LPCHPC LPTHPT

T5

P5

T41

P41 P45m2

m25

m3

m41

m45

m5

Black = typical

Green = optional

Red = advanced

IVHM Technical AccomplishmentElement: Propulsion Health Management

Title: Initial characterization of sensor placement methodologyDate: 9-07

Investigator, Contributors: George Kopasakis, Shane Sowers

Milestone Supported: 1.3.2.1

Type: Demonstration

Description:

-- The Systematic Sensor Selection Strategy (S4) was applied to a large

commercial turbofan engine simulation to select sensors to augment the current

sensor suite.

-- Sensors were selected that demonstrated an improved performance for gas

path diagnostics.-- The demonstration explored which sensor combinations can best detect and

discriminate ten single fault scenarios at the cruise operating point

Outcome/Results:

-- The demonstration compared the diagnostic performance of the current

sensor suite versus the diagnostic performance obtained using S4 to select

additional sensors from a list of optional candidate sensors, and a list of optional

plus advanced sensors.

-- When optional sensors were considered, the best performance was

achieved with 3 additional sensors.-- When optional + advanced sensors were considered, the best

diagnostic performance was achieved with 10 additional sensors.

-- Additional sensors which provide limited or no diagnostic improvement

will cause the overall diagnostic merit value to decrease

Notes:

Baseline sensors = suite of current engine sensors

Optional sensors = current COTS sensing technology available

Advanced sensors = no current COTS technology exists

Figure 1. Turbofan engine with sensor type and

locations.

Figure 2. Diagnostic performance for optimum

sensor suites increasing in size.

0

5

10

15

20

25

30

35

40

45

50

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Number of Additional Sensors

Dia

gnosticMeritValue

Optional Sensors

Opt ional and Advanced Sensors

Optimal sensor suite(optional sensors)

Optimal sensor suite(optional + advanced sensors)

Baseline sensorsuite

3-optimal sensor selection (simon)

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