Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction

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• 作者：A. Mosallam ; K. Medjaher ; N. Zerhouni
• 关键词：Degradation modeling ; Online estimation ; Discrete Bayes filter ; Uncertainty representation ; Data ; driven PHM
• 刊名：Journal of Intelligent Manufacturing
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：27
• 期：5
• 页码：1037-1048
• 全文大小：1,222 KB
• 刊物类别：Business and Economics
• 刊物主题：Economics
  Production and Logistics
  Manufacturing, Machines and Tools
  Automation and Robotics
  
• 出版者：Springer Netherlands
• ISSN：1572-8145
• 卷排序：27

文摘

Reliability of prognostics and health management systems relies upon accurate understanding of critical components’ degradation process to predict the remaining useful life (RUL). Traditionally, degradation process is represented in the form of physical or expert models. Such models require extensive experimentation and verification that are not always feasible. Another approach that builds up knowledge about the system degradation over the time from component sensor data is known as data driven. Data driven models, however, require that sufficient historical data have been collected. In this paper, a two phases data driven method for RUL prediction is presented. In the offline phase, the proposed method builds on finding variables that contain information about the degradation behavior using unsupervised variable selection method. Different health indicators (HIs) are constructed from the selected variables, which represent the degradation as a function of time, and saved in the offline database as reference models. In the online phase, the method finds the most similar offline HI, to the online HI, using k-nearest neighbors classifier to use it as a RUL predictor. The method finally estimates the degradation state using discrete Bayesian filter. The method is verified using battery and turbofan engine degradation simulation data acquired from NASA data repository. The results show the effectiveness of the method in predicting the RUL for both applications.