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An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings

Frontiers of Mechanical Engineering 2018年第2期13卷 301-310页

作者： Yanfeng PENG Junsheng CHENG Yanfei LIU Xuejun LI Zhihua PENG State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha 410082 China College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082 China Hunan-Provincial Key Laboratory of Health Maintenance for Mechanical Equipment Hunan University of Science and Technology Xiangtan 411201 China School of Mathematics and Physics University of South ChinaHengyang 421001 China

A novel data-driven method based on Gaussian mixture model （GMM） and distance evaluation technique （DET） is proposed to predict the remaining useful life （RUL） of rolling bearings. The data sets are clustered by GMM to divide all data sets into several health states adaptively and reasonably. The number of clusters is determined by the minimum description length principle. Thus, either the health state of the data sets or the number of the states is obtained automatically. Meanwhile, the abnormal data sets can be recognized during the clustering process and removed from the training data sets. After obtaining the health states, appropriate features are selected by DET for increasing the classification and prediction accuracy. In the prediction process, each vibration signal is decomposed into several components by empirical mode decomposition. Some common statis- tical parameters of the components are calculated first and then the features are clustered using GMM to divide the data sets into several health states and remove the abnormal data sets. Thereafter, appropriate statistical parameters of the generated components are selected using DET. Finally, least squares support vector machine is utilized to predict the RUL of rolling *** results indicate that the proposed method reliably predicts the RUL of rolling bearings.

关键词： Gaussian mixture model distance evaluation technique health state remaining useful life rolling bearing

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