Gas Turbine Gas Path Diagnostics using Artificial Neural Networks · 2017-10-01 · Gas Turbine Gas Path Diagnostics using Artificial Neural Networks . UNCLASSIFIED UNCLASSIFIED

DGTA-ADF DDAAFS ACPA

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Flight Lieutenant Rashmin Gunaratne

Gas Turbine Gas Path Diagnostics using Artificial Neural Networks

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Overview

• Assumptions • Introduction & Background • Methodology • Engine Model Development • Neural Networks • Results • Conclusions

Overview Assumptions

Introduction & Background Methodology

Engine Model Development Neural Networks

Results Conclusions

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Assumptions

• Sensor faults have not been considered (ignored) • Real data used to model the engine, but not train the neural network • Does not include accessory components extracting power from the engine • Does not include the extraction of bleed air for functions other than turbine cooling • Does not address inefficiencies caused by the propeller due to the lack of data and

capability to simulate it within the chosen software.




Results Conclusions

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Introduction & Background

The Need for Quick Diagnostics

• C130J is the workhorse of the Australian Air Force • Only 10 Aircraft available at any given point in time (taking into

consideration aircraft in DM) • Therefore:

– Must minimise maintenance downtime to increase availability • Engine fault identification ranges from less than an hour to days • Identification depends on:

– Skills of the technician – Corporate knowledge of the person/group working on engine

• A tool or method to narrow down and identify the faulty component and type of fault could reduce downtime




Results Conclusions

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Methodology

5

Develop Engine

DP Modelling in PYTHIA

Test Cell Data

Flight Manual

Data

OD Adaptation in PYTHIA

Test Cell Data

OD Verification

Measurement Selection

NN Sample Generation

Select NN Structure &

Training Algorithm

Identify Fault & Degradation

Cases Train NN

Verify NN

Test Cell Data of

Degraded Engine

GPA through PYTHIA

Create Nested NN

Verify Nested NN

Record Results & Discuss

Flight Manual

Data




Results Conclusions

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Engine Model Development

6

• AE2100D3 engine – C130J Hercules • Engine Modelled utilising PYTHIA • Data used:

– Type Certificate Data Sheet – Post overhaul Ground Run data

• Initially used embedded PYTHIA engine maps – with Design & Off-Design point Adaptation done using engine test cell data

(Gunaratne, 2016)

Model Development




Results Conclusions

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Evaluation Point 1 Evaluation Point 2 Evaluation Point 3 Evaluation Point 4 Evaluation Point 5

PCN 0.88020 0.92348 0.95497 0.98077 1.00

Adapted Error

Not Adapted

Error

Adapted Error

Not Adapted

Error

Adapted Error

Not Adapted

Error

Adapted Error

Not Adapted

Error

Adapted Error

P2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% T2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

P3 (Compressor Outlet Pressure) -7.05% 25.96% -1.95% 18.40% 0.57% 12.75% 0.70% 5.90% 0.00%

T3 (Compressor Outlet Temperature) 0.72% 9.01% 1.09% 5.84% 1.18% 3.95% 0.91% 1.96% 0.00%

Fuel Flow 0.35% 36.93% 3.08% 26.09% 4.40% 18.06% 2.91% 8.64% -0.01% T7 (MGT, ITT) 5.60% 12.82% 3.42% 8.42% 2.63% 5.57% 1.59% 2.69% 0.00%

Shaft Power 0.08% 58.61% 2.67% 38.38% 3.88% 24.77% 2.44% 11.24% 0.00%

Ground DP & OD Condition Verification with Test Cell Results




Results Conclusions

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0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

0.86 0.88 0.9 0.92 0.94 0.96 0.98 1 1.02

Erro

r %

PCN

T3 - Adapted vs Not Adapted

Adapted T3 (Compressor Outlet Temperature)

Not Adapted T3 (Compressor Outlet Temperature)

-10.00%

0.00%

10.00%

20.00%

30.00%

40.00%

0.86 0.88 0.9 0.92 0.94 0.96 0.98 1 1.02

Erro

r %

PCN

Fuel Flow - Adapted vs Not Adapted

Adapted Fuel Flow Not Adapted Fuel Flow

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

0.86 0.88 0.9 0.92 0.94 0.96 0.98 1 1.02

Erro

r %

PCN

Shaft Power - Adapted vs Not Adapted

Adapted Shaft Power Not Adapted Shaft Power

OD Condition Verification




Results Conclusions

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Altitude Operations

Type Altitude (ft)

Mach @ Alt OAT (°C)

Flight Manual Extrapolated Shaft

Power (w)

Simulated Engine Shaft

Power (w) Error

Flight Manual Calculated Fuel

Flow (kgs-1)

Simulated Fuel Flow

(kgs-1) Error

Cruise 10,000 0.16 -5 REDACTED REDACTED 0.00% REDACTED REDACTED 3.18%

Cruise 15,000 0.19 -14.718 REDACTED REDACTED -0.15% REDACTED REDACTED -0.79%



Flight Idle 22,000 0.584 -28.5864 REDACTED REDACTED -5.44% REDACTED REDACTED -11.10%

Ground Operations

Ground Runs -20 REDACTED REDACTED 0.00% REDACTED REDACTED -1.84%

Ground Runs -10 REDACTED REDACTED 0.00% REDACTED REDACTED -1.95%

Ground Runs 0 REDACTED REDACTED 0.00% REDACTED REDACTED -1.15%

Ground Runs 10 REDACTED REDACTED 0.00% REDACTED REDACTED 0.07%



OD Condition Verification




Results Conclusions

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Neural Networks

10

What is a Neural Network




Results Conclusions

Hidden Layer

Input Output

Neurons

• Dendrites receives information (other neurons, nerve cells etc.)

• Nucleus senses the signals and send them out via the axon to the next nucleus

• The output strength of the axons are constant

• The synapses are what gets adjusted to change the strength of the signal received by the nucleus. – These weights can be trained.

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Neural Networks

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Engine Fault Detection NN

CLASS1

Input Temperature

Deviations

Engine Faulty Engine Not Faulty

Target = 1 Target = 0

SCF & DCF Classification

NNCLASS2

SCF DCF


C1 T1

C1 or T1 Classification

NNCLASS3


C1Approximation NN

APPROX2

T1Approximation NN

APPROX3

ηc%Γc%

ηT%ΓT%

T1 Approximation

NNAPPROX1

ηc%Γc%ηT%ΓT%

• Single network alone is insufficient for isolation & Quantification

• Multiple ANN was required • A Nested ANN structure was created • Classification networks to identify the

fault & determine component • Approximation networks for

quantification

Network Structure




Results Conclusions

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Neural Networks

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• Generated via PYTHIA • Aim for 3 component degradation (not including combustor) • MATLAB NN Toolbox & code was used for training

COMP1 TURB1 TURB2 COMP1 + TURB1

COMP1 + TURB2

TURB1 + TURB2

COMP1 + TURB1 + TURB2

η% Γ% ɳ% Γ% η% Γ% η% Γ% η% Γ% η% Γ% η% Γ% COMP1 -7 -7 -5 -5 -5 -5 -5 -5 TURB1 -5 10 -3.1 -6.2 -3.5 -7 -3.9 -7.8 TURB2 -1.7 3.4 -0.7 -1.4 -2 -4 -2.8 -5.6

Step Size: 0.5-1.0 Sample Sizes: Greater than 1,000,000 Large computational resource usage

Training Dataset




Results Conclusions

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Neural Networks

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CLASS3 Network APPROX3 Network Training




Results Conclusions

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Results

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99.50% 94.30%

98.80%

60%65%70%75%80%85%90%95%

100%105%

Classification Network

Pred

ictio

n Ac

cura

cy

Classification Network Prediction Accuracy

CLASS1 CLASS2 CLASS3

00.10.20.30.40.50.60.70.80.9

1

APPROX1 APPROX2 APPROX3

2σ o

f Err

or

Network

Quantification Network Prediction Accuracy

2σ ηC 2σ ΓC 2σ ηT 2σ ΓT

Results - Prediction Accuracy




Results Conclusions

CLASS1 = Faulty or not faulty CLASS2 = SCF or DCF CLASS3 = If SCF, then C1 or T1

APPROX1 = Quantify DCF APPROX2 = Quantify C1 APPROX3 = Quantify T1

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Results

15

-6.00

-5.00

-4.00

-3.00

-2.00

-1.00

0.00

1.00

0 20 40 60 80 100 120

Flow

Deg

rada

tion

%

Test Case

8-40-40-2 Compressor Flow Prediction - C1

Comp Flow Predicted Comp Flow Actual

-6.00

-5.00

-4.00

-3.00

-2.00

-1.00

0.00

1.00

0 20 40 60 80 100 120

Effic

ienc

y De

grad

atio

n %

Test Case

8-40-40-2 Compressor Efficiency Prediction - C1

Compressor Efficiency Predicted Compressor Efficiency Actual

Prediction Accuracy – Compressor Degradation




Results Conclusions

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Results

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-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

1 11 21 31 41 51 61

% D

egra

datio

n

Sample Number

APPROX3 Turbine Efficiency Prediction Comparison

SCF Turbine Efficiency Target SCF Turbine Efficiency Predicted

0

1

2

3

4

5

6

7

1 11 21 31 41 51 61

% D

egra

datio

n

Sample Number

APPROX3 Turbine Flow Prediction Comparison

SCF Turbine Flow Target SCF Turbine Flow Predicted

Prediction Accuracy – Turbine Degradation




Results Conclusions

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Results

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-6-5-4-3-2-10

1 11 21 31 41 51 61 71 81 91 101

% C

ompo

nent

Deg

rada

tion

Compressor Efficiency

DCF Compressor Efficiency Target

DCF Compressor Efficiency Predicted

-6

-5

-4

-3

-2

-1

0

1 11 21 31 41 51 61 71 81 91 101% C

ompo

nent

Deg

rada

tion

Compressor Flow Capacity

DCF Compressor Flow Target DCF Compressor Flow Predicted

01234567

1 11 21 31 41 51 61% C

ompo

nent

Deg

rada

tion

Turbine Flow Capacity

DCF Turbine Flow Target DCF Turbine Flow Predicted

-4

-3

-2

-1

0

1 11 21 31 41 51 61

% C

ompo

nent

Deg

rada

tion

Turbine Efficiency

DCF Turbine Efficiency Target

DCF Turbine Efficiency Predicted

Prediction Accuracy – Compressor & Turbine Degradation




Results Conclusions

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Results

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0

0.1

0.2

0.3

0.4

0.5

0.6

0.7


2σ

Comparison of 2σ for Noise and No Noise Networks

Noise No Noise

Case Study 1 – Use of Noise Free Data




Results Conclusions

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Neural Networks

19

Case Study 2 – Mixed Training Samples

0.35

0.4

0.45

0.5

0.55

0.6

0.65

APPROX 1 APPROX 2

Aver

age

2σ

Using Mixture of Noise and Noise-Free Samples

Noise & No Noise Mixed Noise Only

Results




Results Conclusions

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Neural Networks

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Case Study 3 – Limited Sensor Use

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000


Aver

age

2σ

Comparison of Limited Measurement Sensor Use

8 Inputs 5 Inputs

Results




Results Conclusions

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Conclusion & References

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- The Nested ANN detected, isolated and quantified the engine degradation to a high level. - Multi component fault detection and quantification had a relatively high number of false readings, most likely due to the similarities in component fault signatures. - When trained and verified with noise free degradation samples, the network structure would almost always have the correct output. - ANN based diagnostics system with the current engine measurement set could be viable because: - It would be capable of providing the user with a ‘ball park’ figure of the component degradation. - However, to increase accuracy the RAAF would need to modify the engines with more sensors.

Conclusion




Results Conclusions

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References

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Results Conclusions

Fault signature for type of degradation

Zwebek, A. I., and Pilidis, P., 2003, ‘Degradation Effects on Combined Cycle Power Plant Performance—Part I: Gas Turbine Cycle Component Degradation Effects’, J. Eng. Gas Turbines Power, 125(3), p. 651.

QUESTIONS

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Additional Slides

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Results Conclusions

Conclusion

Fault signature for type of degradation

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Additional Slides

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Results Conclusions

SCF & DCF Fault Signatures

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Additional Slides

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Results Conclusions

SCF & DCF Fault Signatures

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Additional Slides

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Results Conclusions

ANN Drawbacks

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Additional Slides

28




Results Conclusions

Engine station numbering

Documents

Gas Turbine Gas Path Diagnostics using Artificial Neural Networks · 2017-10-01 · Gas Turbine Gas Path Diagnostics using Artificial Neural Networks . UNCLASSIFIED UNCLASSIFIED