Aircraft Fault Detection and Classification Using Multi-Level Immune Learning Detection

1. Aircraft Fault Detection and Classification Using Multi-Level Immune Learning Detection Derek Wong* Scott Poll** Kalmanje KrishnaKumar** *University of Memphis **NASA Ames Research Center

2. Presentation Outline • Problem Definition/Approach • Prototype Domain • Immune System • Negative Selection • Immunity-Based Fault Detection System • Neural Network Classifier • Results • Future Work

3. Problem Definition • Improve situational awareness of failures in the presence of an intelligent control system that is designed to compensate for those failures. • Awareness is required to inform the person of system degradation so that he does not perform an action that might lead to unexpected behavior. • Feed back failure information to controller to enable better handling qualities for degraded aircraft

4. Approach • Fault Detection • Inspired by the human immune system • Negative selection algorithm • Creates detectors in non-self space • Fault Isolation/Classification • Neural network classifiers • Identify pattern of activated detectors

5. Prototype Domain • Transport aircraft similar to Boeing 757 • 6 DOF, non-linear, high-fidelity simulator (FLTz) • Fault tolerant (w/o FDI) neural flight control system • Focus on flight control system failures • Ailerons, elevators, rudder, stabilizer • Hard-to-position (stuck), loss-of-effectiveness (degraded actuation) failures • Neural flight controller failures • Assume no control surface position sensors on aircraft

6. Prototype Domain (cont.) • Consider nominal cruise conditions • Mach: 0.78 – 0.84 • Altitude: 28,000 – 36,000 ft • Turbulence: none – moderate • Fly a sequence of roll and pitch maneuvers

7. Error Signals or Command Augmentation Signals Detector Generation using MILD Fault Classification using MILD Fault Detection and Classification Prototype Domain (cont.)

8. Biological Immune System • Protect living beings from external attacks (bacteria, virus, etc) • Primary response • Innate immune system • Adaptive Immune System • Secondary response • Remember past encounters • Faster response the second time around Infection ReInfection Innate Immunity Adaptive Immunity Specific Immune Memory Disease Recovery No Disease

9. Detector Set Protected Match No S Strings ( ) Yes Non-self Detected Use of Negative Selection 10010 10110 .. 11000 00101 Negative Selection 10110 ... 00101 Censoring Self -Protein 10010, 11000 Random T-cells Detector Set Monitoring

10. F1 Non_Self F4 Self F2 Self F3 Immunity Based Fault Detection Concept Illustration

11. Evolving Fault detectors • Goal: to evolve 'good' fault indicators (detectors) in the non-self (abnormal) space. • 'good' detector means: • It must not cover self. • It has to be as general as possible: the larger the volume, the better. • Collectively provide maximum coverage of the non-self space with minimum overlap • Some detectors serve as specialized (signature for known fault conditions) and others are for probable (or possible) faulty conditions.

12. RNS Algorithm:

13. Different Computational Steps (a) Calculate detector radius (b) Moving a detector (c) Cloning a detector

14. Measure of overlap

15. Self Matured detectors from current iteration Matured detectors from all previous iterations *Candidate detectors from random generation xCandidate detectors from move operation +Candidate detectors from clone operation 2-D Detector Generation Example

16. Self Matured detectors from current iteration Matured detectors from all previous iterations *Candidate detectors from random generation xCandidate detectors from move operation +Candidate detectors from clone operation 2-D Detector Generation Example (cont.)

17. Self Matured detectors from current iteration Matured detectors from all previous iterations *Candidate detectors from random generation xCandidate detectors from move operation +Candidate detectors from clone operation 2-D Detector Generation Example (cont.)

18. Self Matured detectors from current iteration Matured detectors from all previous iterations *Candidate detectors from random generation xCandidate detectors from move operation +Candidate detectors from clone operation 2-D Detector Generation Example (cont.)

19. Self Matured detectors from current iteration Matured detectors from all previous iterations *Candidate detectors from random generation xCandidate detectors from move operation +Candidate detectors from clone operation 2-D Detector Generation Example (cont.)

20. Self Matured detectors from current iteration Matured detectors from all previous iterations *Candidate detectors from random generation xCandidate detectors from move operation +Candidate detectors from clone operation 2-D Detector Generation Example (cont.)

21. Self Matured detectors from current iteration Matured detectors from all previous iterations *Candidate detectors from random generation xCandidate detectors from move operation +Candidate detectors from clone operation 2-D Detector Generation Example (cont.) …after 20 iterations: 188 matured detectors

22. Neural Network Classifier • Multi-layer perceptron with back-propagation algorithm • One network for each fault type • Detectors activated for fault fed to input layer nodes • Hidden layer • Output layer is single node with value [0 1] • Data input to each network, assign class to network with highest output value

23. Associating Detectors with Failures Fault type #1 Fault type #2 Etc…

24. Fault Type Fault Type Activated Detectors Activated Detectors Detection Rate (%) Detection Rate (%) False Alarm Rate (%) False Alarm Rate (%) Mean Mean Std Deviation Std Deviation Mean Mean Std Deviation Std Deviation +L elevator +L elevator 6 6 92.3 94.7 0.88 0.84 1.19 0.49 0.53 0.07 -L elevator -L elevator 7 7 83.2 81.6 0.92 0.94 0.53 1.21 0.08 0.23 +R elevator +R elevator 8 8 92.9 89.9 0.86 0.92 0.38 1.08 0.09 0.36 -R elevator -R elevator 9 9 88.6 85.3 0.89 0.83 1.14 0.37 0.05 0.29 +L aileron +L aileron 4 4 99.5 94.6 0.89 1.06 0 0.26 0.17 0 -L aileron -L aileron 9 9 93.4 97.8 0.98 1.22 0.35 0 0.12 0 +R aileron +R aileron 8 8 96.8 98.7 1.03 1.09 0 0.72 0.11 0 -R aileron -R aileron 11 11 92.1 96.2 0.98 1.11 0.28 0 0.24 0 Detection Results No activation threshold With activation threshold

25. NONE +L elevator -L elevator +R elevator -R elevator +L aileron -L aileron +R aileron -R aileron NONE 1 0 0 0 0 0 0 0 0 +L elevator 0.08 0.82 0.02 0 0.08 0 0 0 0 -L elevator 0.11 0.04 0.79 0.06 0 0 0 0 0 +R elevator 0.06 0 0.07 0.84 0.03 0 0 0 0 -R elevator 0.02 0.07 0.03 0 0.88 0 0 0 0 +L aileron 0.02 0 0 0 0 0.92 0 0.06 0 -L aileron 0.04 0 0 0 0 0 0.89 0 0.07 +R aileron 0.07 0 0 0 0 0.11 0 0.82 0 -R aileron 0.02 0 0 0 0 0 0.04 0 0.92 Classification Results

26. Future Work • Detection/classification results for more fault types • Acquire and analyze data for other flight regimes • Examine sensitivity to different pilots and flight regimes • Examine higher order detector shapes • Explore concept of gene library for detection process