2018, Volume 13, Issue 2
Frontiers of Mechanical Engineering >> 2018, Volume 13, Issue 2 doi: 10.1007/s11465-017-0449-7
An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings
1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China.
2. College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
3. Hunan Provincial Key Laboratory of Health Maintenance for Mecha- nical Equipment, Hunan University of Science and Technology, Xiangtan 411201, China.
4. School of Mathematics and Physics, University of South China, Hengyang 421001, China
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Abstract
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 statistical 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 bearings. Experimental results indicate that the proposed method reliably predicts the RUL of rolling bearings.
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