Motor Bearing Fault Diagnosis Based on Stochastic Resonance Enhanced Stator Current Signals

Wenyue Zhang, Peiming Shi, Dongying Han, Yinghang He, Fengshou Gu, Andrew Ball

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Due to its simple structure and high reliability, induction motors have become a necessary element in the modern production process. Analyzing the current and vibration signals of induction motor is the primary approach for machine condition monitoring and fault diagnosis. Bearing is an important rotating part of induction motor, and its health status directly affects the running state of the motor. The non-intrusively measured stator current contains a wealth of information about the running state of the motor, and a fault will cause a new frequency component to appear in the stator current. However, due to the effects of noise in the signal and the weak nature of the faulty signal, it is hard to observe these components directly from the original signal. Stochastic resonance is an effective technique to enhance weak signals with noise, and it has been widely used in feature enhancement of rotating machinery faults. Therefore, this paper proposes a second-order underdamped tristable stochastic resonance (UTSR) method based on the characteristics of current signals in order to enhance the fault feature of the induction motor bearing. Firstly, the theoretical signal-to-noise ratio (SNR) of the output signals from the model is examined under the excitation of different sinusoidal components added with different levels of Gaussian noise. It shows that the UTSR model can effectively enhance the fault features. Finally, experimental results show that the UTSR method effectively enhance the bearing early fault characteristics in stator current of the motor.

Original languageEnglish
Title of host publicationProceedings of TEPEN 2022
Subtitle of host publicationEfficiency and Performance Engineering Network
EditorsHao Zhang, Yongjian Ji, Tongtong Liu, Xiuquan Sun, Andrew David Ball
PublisherSpringer, Cham
Number of pages11
ISBN (Electronic)9783031261930
ISBN (Print)9783031261923, 9783031261954
Publication statusPublished - 4 Mar 2023
EventInternational Conference of The Efficiency and Performance Engineering Network 2022 - Baotou, China
Duration: 18 Aug 202221 Aug 2022

Publication series

NameMechanisms and Machine Science
Volume129 MMS
ISSN (Print)2211-0984
ISSN (Electronic)2211-0992


ConferenceInternational Conference of The Efficiency and Performance Engineering Network 2022
Abbreviated titleTEPEN 2022
Internet address


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