By: Nasoor Bagheri ([email protected]) Network Security In the name of god.
An Adaptive Modeling for Robust Prognostics on a Reconfigurable Platform Behrad Bagheri Linxia Liao.
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Transcript of An Adaptive Modeling for Robust Prognostics on a Reconfigurable Platform Behrad Bagheri Linxia Liao.
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