Research on Rolling Bearing On-Line Fault Diagnosis Based on Multi-dimensional Feature Extraction

  • Tianwen ZhangEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


In the paper, a novel rolling bearing fault diagnostic method was proposed to fulfill the requirements for effective assessment of different fault types and severities with real-time computational performance. Firstly, multi-dimensional feature extraction is discussed. And secondly, a gray relation algorithm was used to acquire basic belief assignments. Finally, the basic belief assignments were fused through Yager algorithm. The related experimental study has illustrated the proposed method can effectively and efficiently recognize various fault types and severities.


Rolling element bearing Pattern recognition Gray relation algorithm Yager algorithm 



This work is supported by the National Natural Science Foundation of China (61771154) and funding of State Key Laboratory of CEMEE (CEMEE2018K0104A).

Meantime, all the authors declare that there is no conflict of interests regarding the publication of this article.

We gratefully thank of very useful discussions of reviewers.


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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.College of Information and Communication EngineeringHarbin Engineering UniversityHarbinChina

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