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Journal of Sensors
Volume 2017, Article ID 6737295, 14 pages
Research Article

Improving Rolling Bearing Fault Diagnosis by DS Evidence Theory Based Fusion Model

1Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China
2School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
3Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA

Correspondence should be addressed to Shaobo Li; nc.ude.uzg@oboahsil

Received 28 May 2017; Revised 30 August 2017; Accepted 17 September 2017; Published 22 October 2017

Academic Editor: Guiyun Tian

Copyright © 2017 Xuemei Yao 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.


Rolling bearing plays an important role in rotating machinery and its working condition directly affects the equipment efficiency. While dozens of methods have been proposed for real-time bearing fault diagnosis and monitoring, the fault classification accuracy of existing algorithms is still not satisfactory. This work presents a novel algorithm fusion model based on principal component analysis and Dempster-Shafer evidence theory for rolling bearing fault diagnosis. It combines the advantages of the learning vector quantization (LVQ) neural network model and the decision tree model. Experiments under three different spinning bearing speeds and two different crack sizes show that our fusion model has better performance and higher accuracy than either of the base classification models for rolling bearing fault diagnosis, which is achieved via synergic prediction from both types of models.