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. 2 Summary Objectives SVM The Method Case Study Implementation Conclusions

3. 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.

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

8. The Method 8 Training Generalization

9. The Method 1. Training the Classifier2. 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 1.Collection of data:  Flight data: Manif. Press., Manif. Temp., N2 (high pressure compressor speed)  Maintenance Logs: Left Bleed Valve Replacements (date/time) 12 Flight i 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

13. Implementation 13 Optimum Decision Surface Example with 2 parameters

14. Implementation 1.Generalization for new flights  Observation: The rate of UNHEALTHY seems to increase up to a replacement 14 Replacements HEALTHY UNHEALTHY Time (days)

15. Implementation 1.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 AAircraft B 16 Only data from AIRCRAFT A was used to train the SVM.

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