JET ENGINE SENSOR VALIDATION WITH PROBABILISTIC NEURAL NETWORKS

Transcription

1 JET ENGINE SENSOR VALIDATION WITH PROBABILISTIC NEURAL NETWORKS C Romessis Research Assistant K Mathioudakis Associate Professor Laboratory of Thermal Turbomachines National Technical University of Athens 1

2 JET ENGINE SENSOR VALIDATION WITH PROBABILISTIC NEURAL NETWORKS Definition of the diagnostic problem Probabilistic Neural Network Architecture PNN sensor validation o Sensor fault detection in a faulty engine o Minimum detectable sensor biases o Sensor fault detection in a deteriorating engine o Sensor validation during a flight o Multiple sensor faults Summary - Conclusions 2

3 JET ENGINE SENSOR VALIDATION WITH PROBABILISTIC NEURAL NETWORKS Definition of the diagnostic problem Probabilistic Neural Network Architecture PNN sensor validation o Sensor fault detection in a faulty engine o Minimum detectable sensor biases o Sensor fault detection in a deteriorating engine o Sensor validation during a flight o Multiple sensor faults Summary - Conclusions 3

4 Definition of the Diagnostic Problem Determine the bias of the readings from a number of instruments XNLP XNHP P 13 T 13 P 3 T 3 T SW 2 SW 26 SW 41 SE 12 SE 2 SE 26 SE 41 SW 12 SW 49 SE 49 A 8 High-by-Pass ratio, partially mixed, turbofan engine used as a test case 4

5 JET ENGINE SENSOR VALIDATION WITH PROBABILISTIC NEURAL NETWORKS Definition of the diagnostic problem Probabilistic Neural Network Architecture PNN sensor validation o Sensor fault detection in a faulty engine o Minimum detectable sensor biases o Sensor fault detection in a deteriorating engine o Sensor validation during a flight o Multiple sensor faults Summary - Conclusions 5

6 Input Layer Diagnosis with the Probabilistic Neural Network (PNN) Hidden Layer Output Layer Input information x 1 x 2 x n-1 x n 1 2 j m-2 m-1 m S 1 S i S k Output probabilities Compare Probabilities Diagnosis 6

7 Structure of the PNN Input layer: Deltas of the measurements Δ(XNLP) Δ(XNHP) Δ(P13) Δ(P3) Δ(T3) Δ(T5) Δ(T13) XNLP XNHP P13 P3 T3 T5 T13 7

8 Pattern Generation from Measurements XNLP, XNHP, P13, P3, T3, T5, T13 Pamb, P1, T1, WFE EPM Δ(Y)=(Y-Ynom)/Ynom Nominal values: XNLP, XNHP, P13, P3, T3, T5, T13 Pattern Δ(XNLP) Δ(XNHP) Δ(P13) Δ(P3) Δ(T3) Δ(T5) Δ(T13) 8

9 Turbofan Engine Modeling Quantities defining the operating Conditions: Ambient Pressure Fuel consumption Engine Inlet Conditions (pressure, temperature) Fault Parameters: Flow factors along the engine Efficiency factors along the engine EPM Exhaust area Measured quantities: Shafts speed (low and high pressure) Pressures and temperatures along the engine 9

10 XNLP Structure of the PNN Hidden layer: Training patterns XNHP P13 P3 T3 T5 T13 m ~ 30,000 nodes m-3 m-2 m-1 m Each node: : A Noise-free pattern produced by simulation Ok P1 T1 WFE XNLP XNHP P13 P3 T3 T6 T13 10

11 Structure of the PNN Output layer: Considered classes 12 (classes/sensor) X 10 (sensors)=120 classes Ok P1=-3% WFE=-3% WFE=-25% WFE=-2% WFE=3% XNLP=-3% T5=2% T13=3% No fault probability probability of sensor biases Example 530% 220% 465% 365% 6830% 158% 360% 123% 290% Fuel flow reading has a bias of -2% 11

12 Materializing the Network + u Generation of the patterns sensor fault in u u' + Noise σ u u* Faulty operation; some f i 0 EPM Network once trained, then tested Healthy operation; all f i =0 EPM sensor fault in Y + Y' Y Y o * Noise σ Y + Y * ΔY=(Y * -Y o * )/Y o *

13 Aspects Examined to Assess Diagnostic Potential Simultaneous presence of Component Faults Minimum detectable sensor biases Drifting Deterioration of Fault Parameters Diagnosis at different Operating Conditions Multiple Sensor Faults detection 13

14 Aspects Examined to Assess Diagnostic Potential Have been considered for: A Patterns for training the network B Patterns for testing the network 14

15 Setting Up OfO A Probabilistic Neural Network For Sensor Fault Detection Including Operation With Component Faults Definition of the diagnostic problem Probabilistic Neural Network Architecture PNN sensor validation o Sensor fault detection in a faulty engine o Minimum detectable sensor biases o Sensor fault detection in a deteriorating engine o Sensor validation during a flight o Multiple sensor faults Summary - Conclusions 15

16 Examples of Test Patterns XNLP XNHP P13 P3 T3 T6 T13 Healthy engine, Healthy sensors Faulty engine, Healthy sensors Healthy engine, Faulty sensor sensor (ΔXNLP= XNLP=-3%) Faulty engine, Faulty sensor (ΔΤ ΔΤ3= 3=-3% 3%) 16

17 Simultaneous presence of Component Faults How the diagnostic ability is affected at the simultaneous presence of Component Faults? Sensor Biases are detectable in almost all cases of faulty operation of the engine (deviation of 1%-2% of the fault parameters) 17

18 Sensor fault detection in a faulty engine 100 Scores (%) Sensor Bias Detection Faulty Sensor Detection Test Case count 18

19 Minimum detectable sensor biases Which are the minimum sensor biases that can be detected? Biases greater than 04% - 10% are almost always detected for all sensors Bias Levels usually represent 2-6 times the considered noise levels 19

20 Minimum detectable sensor biases % sensor bias a) MDL T1 bias Test Case Count % sensor bias b) MDL WFE bias Test Case Count

21 Drifting Deterioration of Fault Parameters How the diagnostic ability is affected in a deteriorated engine? The general trend is that almost all biases are detectable for deterioration levels of up to ±05% fault parameters deviation 21

22 Sensor fault detection in a deteriorating engine Faulty Sensor detection Score (%) Sensor Bias detection I

23 Diagnosis at different Operating Conditions How the diagnostic ability is affected at different operating conditions? Diagnostic ability unaffected for a region of operating conditions A whole flight envelope can be covered by two PNNs 23

24 Effect of Operating Conditions Zaltm (km) Representation of a flight envelope OP index

25 Effect of Operating Conditions Score(%) Sensor Bias Detection Faulty Sensor Detection OP index

26 Effect of Operating Conditions Score(%) Sensor Bias Detection Faulty Sensor Detection point of network switch OP index

27 Multiple Sensor Faults detection How, possibly, multiple sensor faults can be detected? Faults in up to two different sensors are detected efficiently 27

28 Multiple sensor faults XNLP XNHP P 13 T 13 P 3 T 3 T SW 2 SW 26 SW 41 SE 12 SE SE 26 SE A SE 49 SW 12 SW 49 28

29 Multiple sensor faults: Diagnostic procedure Initial Pattern Diagnosis P(XNLP=-2%)>50% Diagnosis P(P13=1%)>50% Modified Pattern Modified Pattern Diagnosis P(Ok)>50% 29

30 Success rates for Multiple Sensor Faults 100% 80% All actually deviated sensors detected 60% 40% All actually deviated sensors detected + others 20% 0% A B C A: SF on 2 monitoring sensors B: SF on 2 set-point or monitoring sensors C: All combinations of SF on 2, 3, 4 sensors Sensors, not actually deviated, detected 30

31 Conclusions - Results Flexible and easy to built network Wide range of effective diagnosis Satisfactory Minimum Detectable Biases Cases of Multiple sensor faults handled efficiently Robustness in the presence of component faults or deterioration 31