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International Journal of Rotating Machinery
Volume 2017, Article ID 2384184, 10 pages
https://doi.org/10.1155/2017/2384184
Research Article

Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis

1School of Urban Rail Transportation, Soochow University, Suzhou 215131, China
2School of Mechanical and Electric Engineering, Soochow University, Suzhou 215131, China

Correspondence should be addressed to Changqing Shen; nc.ude.adus@nehsqc

Received 2 December 2016; Revised 18 February 2017; Accepted 23 February 2017; Published 13 March 2017

Academic Editor: Pavan K. Kankar

Copyright © 2017 Jun Shuai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Numerous studies on fault diagnosis have been conducted in recent years because the timely and correct detection of machine fault effectively minimizes the damage resulting in the unexpected breakdown of machineries. The mathematical morphological analysis has been performed to denoise raw signal. However, the improper choice of the length of the structure element (SE) will substantially influence the effectiveness of fault feature extraction. Moreover, the classification of fault type is a significant step in intelligent fault diagnosis, and many techniques have already been developed, such as support vector machine (SVM). This study proposes an intelligent fault diagnosis strategy that combines the extraction of morphological feature and support vector regression (SVR) classifier. The vibration signal is first processed using various scales of morphological analysis, where the length of SE is determined adaptively. Thereafter, nine statistical features are extracted from the processed signal. Lastly, an SVR classifier is used to identify the health condition of the machinery. The effectiveness of the proposed scheme is validated using the data set from a bearing test rig. Results show the high accuracy of the proposed method despite the influence of noise.