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Adaptive Extraction Method Based on Time-Frequency Images for Fault Diagnosis in Rolling Bearings of Motor

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  • Yunchao Ma
  • Chengdong Wang
  • Dongchen Yang
  • Cheng Wang

Abstract

In order to diagnose the faults of rolling bearings in motors via time-frequency analysis of bearing vibration signals quickly, this paper puts forward a method of extracting the main components from time-frequency images. A threshold is adaptively determined based on the gray histogram feature of the time-frequency images obtained from the vibration signals of the motor rolling bearings. Then, a mask template is generated by the threshold and a binarization processing. Based on a multiplication operation between the mask template and the original time-frequency image, the signal component with low energy in the time-frequency image is filtered out, and only the main components with high energy is remained for fault diagnosis, which is convenient for the subsequent identification of the faults for motor rolling bearings. The main components in the time-frequency images can be retained adaptively with the thresholds determined by the time-frequency images themselves.

Suggested Citation

  • Yunchao Ma & Chengdong Wang & Dongchen Yang & Cheng Wang, 2021. "Adaptive Extraction Method Based on Time-Frequency Images for Fault Diagnosis in Rolling Bearings of Motor," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, February.
    • Handle: RePEc:hin:jnlmpe:6687195
    DOI: 10.1155/2021/6687195
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