Prognostics of Aircraft Bleed Valves Using a SVM Classification Algorithm

1. Prognostics of Aircraft Bleed Valves Using a SVM Classification Algorithm Renato de Pádua Moreira Cairo L. Nascimento Jr. Instituto Tecnológico de Aeronáutica São José dos Campos - Brazil

2. Summary • Objectives • SVM • The Method • Case Study • Implementation • Conclusions 2

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. 3

4. 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

5. Support Vector Machines 5

6. 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

7. Support Vector Machines • Non-linearly separable universe  Mapping in the Feature Space • Use of Kernel Functions: K(x,xi) = φT(x) φ (xi) 7

8. The Method Training Generalization 8

9. The Method 1. Training the Classifier 2. Generalizing for new flights 9

10. 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

11. Case Study: Aircraft Bleed Valve 11

12. Implementation • Collection of data: • Flight data: Manif. Press., Manif. Temp., N2 (high pressure compressor speed) • Maintenance Logs: Left Bleed Valve Replacements (date/time) • Windowing the flight At least 20 minutes of stable cruise • Extraction of 8 Characteristics Time 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 Flight i 12

13. Optimum Decision Surface Implementation Example with 2 parameters 13

14. Replacements HEALTHY UNHEALTHY Time (days) Implementation • Generalization for new flights • Observation: The rate of UNHEALTHY seems to increase up to a replacement 14

15. Implementation • 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 15

16. Results Aircraft A Aircraft B Only data from AIRCRAFT A was used to train the SVM. 16

17. 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