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Open AccessArticle

A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method

1
The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150001, China
2
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(11), 2356; https://doi.org/10.3390/app9112356
Received: 26 May 2019 / Revised: 29 May 2019 / Accepted: 4 June 2019 / Published: 8 June 2019
(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics)
To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and a multiclass relevance vector machine is proposed in this paper. A fast sample entropy calculation method based on a kd tree is adopted to improve the real-time performance of fault detection in this paper. In view of the non-linearity and non-stationarity of the vibration signals, the vibration signal of the rolling bearing is decomposed into several sub-signals containing fault information by using a wavelet packet. Then, the energy entropy values of the sub-signals decomposed by the wavelet packet are calculated to generate the feature vectors for describing different fault types and severity levels of rolling bearings. The multiclass relevance vector machine modeled by the feature vectors of different fault types and severity levels is used to realize fault type identification and a fault severity analysis of the bearings. The proposed fault diagnosis and severity analysis method is fully evaluated by experiments. The experimental results demonstrate that the fault detection method based on the sample entropy can effectively detect rolling bearing failure. The fault feature extraction method based on the wavelet packet energy entropy can effectively extract the fault features of vibration signals and a multiclass relevance vector machine can identify the fault type and severity by means of the fault features contained in these signals. Compared with some existing bearing rolling fault diagnosis methods, the proposed method is excellent for fault diagnosis and severity analysis and improves the fault identification rate reaching as high as 99.47%. View Full-Text
Keywords: rolling bearing; fault diagnosis; fault severity; sample entropy; wavelet packet energy entropy; multiclass relevance vector machine rolling bearing; fault diagnosis; fault severity; sample entropy; wavelet packet energy entropy; multiclass relevance vector machine
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Chen, Y.; Zhang, T.; Luo, Z.; Sun, K. A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method. Appl. Sci. 2019, 9, 2356.

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