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Journal of Sensors
Volume 2017, Article ID 8092691, 15 pages
https://doi.org/10.1155/2017/8092691
Research Article

Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine

State Key Lab of Power Systems, Department of Thermal Engineering, Tsinghua University, Beijing 100084, China

Correspondence should be addressed to Xiaochen Zhang; moc.liamtoh@8002hcxgnahz

Received 17 January 2017; Revised 27 June 2017; Accepted 20 September 2017; Published 22 October 2017

Academic Editor: Pietro Siciliano

Copyright © 2017 Xiaochen Zhang 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

To diagnose rotating machinery fault for imbalanced data, a method based on fast clustering algorithm (FCA) and support vector machine (SVM) was proposed. Combined with variational mode decomposition (VMD) and principal component analysis (PCA), sensitive features of the rotating machinery fault were obtained and constituted the imbalanced fault sample set. Next, a fast clustering algorithm was adopted to reduce the number of the majority data from the imbalanced fault sample set. Consequently, the balanced fault sample set consisted of the clustered data and the minority data from the imbalanced fault sample set. After that, SVM was trained with the balanced fault sample set and tested with the imbalanced fault sample set so the fault diagnosis model of the rotating machinery could be obtained. Finally, the gearbox fault data set and the rolling bearing fault data set were adopted to test the fault diagnosis model. The experimental results showed that the fault diagnosis model could effectively diagnose the rotating machinery fault for imbalanced data.