A model-based method for remaining useful life prediction of machinery
Lei Yaguo , Li Naipeng , Szymon Gontarz , Lin Jing , Stanisław Radkowski , Jacek Dybała
AbstractRemaining useful life (RUL) prediction allows for predictive maintenance of machinery, thus reducing costly unscheduled maintenance. Therefore, RUL prediction of machinery appears to be a hot issue attracting more andmore attention as well as being of great challenge. This paper proposes a model-based method for predicting RUL of machinery. The method includes two modules, i.e., indicator construction and RUL prediction. In the first module, a new health indicator named weighted minimum quantization error is constructed, which fuses mutual information from multiple features and properly correlates to the degradation processes of machinery. In the second module, model parameters are initialized using the maximum-likelihood estimation algorithm and RUL is predicted using a particle filtering-based algorithm. The proposed method is demonstrated using vibration signals from accelerated degradation tests of rolling element bearings. The prediction result identifies the effectiveness of the proposed method in predicting RUL of machinery.
|Journal series||IEEE Transactions on Reliability, ISSN 0018-9529|
|Publication size in sheets||0.6|
|Keywords in English||health indicator, parameter initialization, particle filtering, remaining useful life (RUL) prediction|
|Score|| = 40.0, 29-11-2017, ArticleFromJournal|
= 40.0, 29-11-2017, ArticleFromJournal
|Publication indicators||: 2016 = 2.79 (2) - 2016=3.202 (5)|
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