A joint particle filter and expectation maximization approach to machine condition prognosis
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This paper presents a probabilistic model based approach for machinery condition prognosis based on particle filter by integrating physical knowledge with in-process measurements into a state space framework to account for uncertainty and nonlinearity in machinery degradation process. One limitation of conventional particle filter is that condition prognosis is performed based on the model with predetermined parameters obtained from simulation studies or lab-controlled tests. Due to the stochastic nature of machinery defect propagation under varying operating conditions, model parameters may vary in practice which causes prediction errors. To address it, an integrated state prediction and parameter estimation framework based on particle filter and expectation-maximization algorithm is formulated and investigated. The model parameters are adaptively estimated based on expectation-maximization algorithm utilizing hidden degradation state and available in-process measurements. Particle filter is then performed on the identified model with estimated parameters following Bayesian inference scheme to improve the robustness and accuracy of machinery condition prognosis. The effectiveness of the developed method is demonstrated through a simulation study and an experimental run-to-failure bearing test in a wind turbine.
KeywordsMachinery condition prognosis Particle filter Parameter estimation Expectation-maximization
This work has been partially supported by the National Science Foundation of US (CMMI-1300999), the National Science foundation of China (Nos. 51504274 and 51674277), the National Key Research and Development Program of China (No. 2016YFC0802105), and the Science Foundation of China University of Petroleum (Nos. 2462014YJRC039 and 2462015YQ0403). Experimental support from Dr. Eric Bechhoefer (NRG Systems Company), and support from the Xi’an Jiaotong University for Zhaoyan Fan are sincerely appreciated. The authors would like to thank the anonymous reviewers for their constructive comments, which have helped improve the paper.
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