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

2 ► 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 About the Author

3 1. Introduction 2. State of the art 3. Degradation Status Assessment 4. A Framework for Prediction Model Selection Based on Reinforcement Learning 5. A Novel Density Estimation Method to Improve the Accuracy of Confidence Value Calculation 6. Design of a Reconfigurable Prognostics Platform (RPP) 7. Conclusion and Future Work Outline 28 March 2013

4 ► 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 Assumptions and Challenges

5 Degradation Status Assessment

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

7 ► 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 Case Study – Bearing Run-to-Failure

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

9 A Framework for Prediction Model Selection Based on Reinforcement Learning

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

11 ► 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. Elements of Proposed Method

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

16 Case Study 2 - Results

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

18 ► 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 Calculation of CV – Boosting Algorithm for GMM T: Number of Mixtures x: training dataset α n : coefficient for each h(x) h(x): weak learner

19 Case Study – Bearing Run-to-Failure ► DLL value for Boosting GMM shows that this algorithm has better performance than two other methods ► 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

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 ► 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 Conclusion and Future Work Conclusion 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