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Fault feature extraction method of pump data sample under strong impact and strong noise environment

Author

Listed:
  • Changming Liu
  • Zhuang Wu
  • Yuewen Huang
  • Wei Mao

Abstract

The condition monitoring and fault diagnosis of pump stations are essential to ensure the normal operation of pump stations. This work monitors the vibration, swing, pressure pulsation, noise, speed of the pump unit in the steady and transient operation process and diagnoses the equipment state to judge the safety and health of the unit. The fault feature extraction method suitable for the environment of strong impact and strong noise is studied. The frequency band segmentation, accuracy chart to determine the resonance frequency band and the maximum correlation kurtosis deconvolution method are used to enhance the fault feature. Finally, the data measured are sampled and selected, and the kurtosis of the system software is measured the skewness and other indicators are compared with the indicators of handheld analysis and measurement data. The total accuracy rate of data collected by the system software reaches 98.22%.

Suggested Citation

  • Changming Liu & Zhuang Wu & Yuewen Huang & Wei Mao, 2023. "Fault feature extraction method of pump data sample under strong impact and strong noise environment," International Journal of Service and Computing Oriented Manufacturing, Inderscience Enterprises Ltd, vol. 4(2), pages 104-114.
    • Handle: RePEc:ids:ijscom:v:4:y:2023:i:2:p:104-114
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