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Automatic fault diagnosis method for mining hydraulic support based on fuzzy analysis

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  • Minjie Chen
  • Rongjin Yang

Abstract

A fuzzy analysis based automatic fault diagnosis method for mining hydraulic supports is proposed with the goal of improving fault detection rate, reducing misdiagnosis rate, and improving diagnostic efficiency. Firstly, for the structure of mining hydraulic support, real-time data collection of mining hydraulic support work is carried out through multiple DS18B20 sensor arrays. Then, the moving average method is used to denoise the collected data, and the artificial bee colony algorithm is used to extract fault features. Finally, based on the results of data denoising and feature extraction, a fuzzy rule library is established using expert experience and knowledge, and fault diagnosis is achieved through fuzzy reasoning and deblurring. The experimental results show that the highest fault detection rate of the proposed method is 83.5%, and the misdiagnosis rate remains below 2%, with a relatively short fault diagnosis time.

Suggested Citation

  • Minjie Chen & Rongjin Yang, 2026. "Automatic fault diagnosis method for mining hydraulic support based on fuzzy analysis," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 40(1/2), pages 78-91.
    • Handle: RePEc:ids:ijmtma:v:40:y:2026:i:1/2:p:78-91
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