Research on Fault Feature Extraction and Recognition of Rolling Bearings


In the field of system health management, the quality of rolling equipment is very important. Therefore, the fault diagnosis of rolling bearings has become a hot research topic. In this paper, the traditional fault feature extraction method is used to optimize the non-linear and non-stationary characteristics of the bearing vibration signal. Furthermore, in order to improve the performance of the fault diagnosis, a novel signal fingerprint is proposed to recognize the fault type. The simulation result show that the new method is successful and effective, and the recognition rate can be improved up to 95.33%, which is better than the traditional methods.

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Conflict of interest

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.


This work is supported by the National Natural Science Foundation of China (Grant No.51909125), Zhejiang Province Public Welfare Technology Application Research Project (No.LGF20E060001) and the K.C. Wong Magna Fund in Ningbo University.

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Correspondence to Guochun Xu.

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Shi, F., Xu, G. Research on Fault Feature Extraction and Recognition of Rolling Bearings. Mobile Netw Appl (2020).

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  • Fault diagnosis
  • Feature extraction
  • Feature selection
  • Fault recognition
  • Signal fingerprint