An Adaptive Modeling for Robust Prognostics on a Reconfigurable Platform

Behrad Bagheri

Linxia Liao

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► 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

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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

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► 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

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Degradation Status Assessment

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► Feature Extraction from Vibration Signals

Degradation Status Assessment

► Dimension Reduction -> PCA

► Evaluate Degradation Status by SOM

» MQE Health Assessment

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► 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

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► 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

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A Framework for Prediction Model Selection Based on Reinforcement Learning

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» Adaptively choose the best prediction model for predicting the feature for each step

Description of the Concept

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► 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

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► 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

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Case Study – Bearing Run-to-Failure

► 6 ARMA models and 1 Linear model are used for prediction► 9 States, prediction for 20 Steps

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► 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

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► 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.

Second Case Study - Spindle

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Case Study 2 - Results

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

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► 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

αn: coefficient for each h(x)

h(x): weak learner

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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

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Design of a Reconfigurable Prognostics Platform (RPP)

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Reconfigurable Prognostics Platform (RPP)

SA: System Agent

KA: Knowledge Agent

EA: Executive Agent

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► Two case studies for RPP evaluation

ATC Health Monitoring Spindle Bearing Health Monitoring

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Evaluating RPP with Case Studies

► Steps and related spent times in reconfiguring server for new request

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► 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

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Thank You