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

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.

Keywords

Gaussian mixture model / distance evaluation technique / health state / remaining useful life / rolling bearing

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Acknowledgments

The authors gratefully acknowledge the support of the National Key Research and Development Program of China (Grant No. 2016YFF0203400), the National Natural Science Foundation of China (Grant Nos. 51575168 and 51375152), the Project of National Science and Technology Supporting Plan (Grant No. 2015BAF32B03), and the Science Research Key Program of Educational Department of Hunan Province of China (Grant No. 16A180). The authors appreciate the support provided by the Collaborative Innovation Center of Intelligent New Energy Vehicle, the Hunan Collaborative Innovation Center for Green Car.

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2017 Higher Education Press and Springer-Verlag Berlin Heidelberg