An Adaptive Modeling for Robust Prognostics on a Reconfigurable Platform

1. An Adaptive Modeling for Robust Prognostics on a Reconfigurable Platform Behrad Bagheri Linxia Liao

2. About the Author • Linxia Liao • B. Sc. In Mechanical Science & Engineering, 2001, HUST, Wuhn, China • M. Sc. Mechanical Science & Engineering, 2004, Huazhong University of S&T. • Ph.D. Mechanical Engineering, 2010, University of Cincinnati • Internship at Harley-Davidson Motor Company • Visiting Scholar at Siemens Corporate Research • Research scientist at Siemens Corporate Research

3. Outline • Introduction • State of the art • Degradation Status Assessment • A Framework for Prediction Model Selection Based on Reinforcement Learning • A Novel Density Estimation Method to Improve the Accuracy of Confidence Value Calculation • Design of a Reconfigurable Prognostics Platform (RPP) • Conclusion and Future Work 28 March 2013

4. Assumptions and Challenges • Assumptions • Certain Vibration Signals can indicate the health of a system • A confidence value threshold can be set to indicate acceptable performance or a serious failure • The system being monitored is degrading gradually in an observable and measurable way. • The baseline is consistent for a certain period of time

5. Degradation Status Assessment

6. Degradation Status Assessment • Feature Extraction from Vibration Signals • Dimension Reduction -> PCA • Evaluate Degradation Status by SOM • MQE Health Assessment

7. Case Study – Bearing Run-to-Failure • Experiment Configuration • Two ICP Accelerometers for each bearing • Sampling Frequency 20 kHz, Sampled every 10 minutes for 2 seconds • A magnetic plug in the oil, used as evidence of system degradation(Amount of debris on the magnetic plug increases when bearing wore out) • Feature Extraction (11 Features) • Dimension Reduction • Top two principal components with 90% of variance

8. Case Study – Bearing Run-to-Failure • SOM-MQE Degradation Status Assessment • First 500 cycles used as baseline data • 4 sections could be distinguished in the MQE plot

9. A Framework for Prediction Model Selection Based on Reinforcement Learning

10. Description of the Concept • Adaptively choose the best prediction model for predicting the feature for each step

11. Elements of Proposed Method • Elements of Reinforcement Learning • Environment: Historical data from database. • Action: the ARMA model used for prediction • State: different degradation states determined by MQE values • State Transition • Reward: A function related to prediction accuracy.

12. Reinforcement Learning trains an agent to interact with the dynamic Environment • The target is to maximize reward in a long run of trial and errors • Look-up table created by Q-Values is used to select models

13. Case Study – Bearing Run-to-Failure • 6 ARMA models and 1 Linear model are used for prediction • 9 States, prediction for 20 Steps

14. Using the results of 3 runs is more reasonable in selecting model • In case that for the same state more than one model have the same probability, Occam’s razor principle could which states the simplest model should be selected

15. Second Case Study - Spindle • First principle component of input data is used for prediction. • 3rd run is used for training (Environment) and 11th run is used for testing • 10 states are defined in one run along with 4 ARMA models.

16. Case Study 2 - Results

17. A Novel Density Estimation Method to Improve the Accuracy of Confidence Value Calculation

18. Calculation of CV – Boosting Algorithm for GMM • Study the distribution of predicted features and comparison with the distribution of baseline data will result in calculating CV value. • Boosting Algorithm of Gaussian Mixture Model (GMM) • PSO is used to optimize the selection of Gaussian models T: Number of Mixtures x: training dataset αn: coefficient for each h(x) h(x): weak learner

19. Case Study – Bearing Run-to-Failure • Feature values for next 20 steps are predicted using the Boosted GMM, GMM with PSO and GMM Only methods • Red dots show the predicted values, black and purple dots show high and low 95% confidence boundaries • DLL value for Boosting GMM shows that this algorithm has better performance than two other methods

20. Design of a Reconfigurable Prognostics Platform (RPP)

21. Reconfigurable Prognostics Platform (RPP) SA: System Agent KA: Knowledge Agent EA: Executive Agent

22. Two case studies for RPP evaluation ATC Health Monitoring Spindle Bearing Health Monitoring

23. Evaluating RPP with Case Studies • Steps and related spent times in reconfiguring server for new request

24. Conclusion and Future Work Conclusion • SOM MQE method can provide a quantitative measure of the machine degradation with only baseline data • The reinforcement learning framework utilized ARMA models as local prediction agents. The proposed method selects appropriate prediction model to gain better prediction accuracy • The proposed density boosting method to convert prediction results of the feature space into confidence value yields more accurate estimation of CV Value Future Work • Identifying the critical components of the complex systems. • Considering more signal processing methods to prepare raw signals • Platform synchronization & standardization

25. Thank You