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| Degradation Data-Driven Remaining Useful Life Estimation Approach under Collaboration between Bayesian Updating and EM Algorithm |
| SI Xiao-Sheng1,3,HU Chang-Hua1,LI Juan2,CHEN Mao-Yin3 |
1. Department of Automation,Xian Institute of High-Technology,Xian 710025
2.College of Electromechanical Engineering,Qingdao Agricultural University,Qingdao 266109
3.Department of Automation,Tsinghua University,Beijing 100084 |
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Abstract Remaining useful life (RUL) estimation is one of the key issues in condition-based maintenance and prognostics and health management. To achieve degradation modeling and RUL estimation for the individual equipment in service,a degradation data-driven RUL estimation approach under the collaboration between Bayesian updating and expectation maximization (EM) algorithm is presented. Firstly,an exponential-like degradation model is utilized to describe the equipment degradation process and the stochastic parameters in the model are updated by Bayesian approach. Based on the Bayesian updating results,the probability distribution of the RUL is derived and the point estimation of the RUL is obtained accordingly. Secondly,based on the monitored degradation data to date,a parameter estimation approach for other non-stochastic parameters in the established degradation model is proved. Furthermore,it is proved that the obtained estimation in each iteration is unique and optimal. Finally,a numerical example and a practical case study are provided to show that the presented approach effectively models degradation process for the individual equipment,achieves RUL estimation,estimates the model parameters and generates better results than a previously reported approach in the literature.
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Received: 13 February 2012
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2013, Vol. 26 
