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<p>In response to the monitoring needs of conveyor roller faults in open-pit mines, this paper proposes a fiber optic sensing roller fault diagnosis method based on dynamic model driving. The study first analyzed the coupling mechanism between fiber optic scattering light field and mechanical vibration, and constructed a non-invasive real-time monitoring system. To address the inherent vibration issues caused by equipment structural damage in open-pit mines under extreme weather conditions, a diagnostic method driven by dynamic models is proposed. By constructing a dynamic model of the conveyor system, the mathematical model of the vibration amplitude obtained from theoretical calculations is mixed and analyzed with the light converted vibration signals collected on site. Short time Fourier transform (STFT) is used for signal conversion, and Mel algorithm is used for preprocessing. By combining an improved GoogLeNet LSTM hybrid deep learning model, the preprocessed signals were trained for classification and recognition, and a database of roller fault features was successfully constructed. The experimental results show that the improved DY GougLeNet LSTM model has a testing accuracy of 96.7% in roller fault diagnosis, which is effectively improved compared to the basic model (CNN) with an accuracy of 83.7%. The relevant achievements provide reliable technical support for unmanned operation of open-pit mine conveying systems.</p>

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Research on fault diagnosis method of fiber optic sensing roller driven by dynamic model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月23日

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  • Hongyu Zhang,
  • Zhiyong Yang,
  • Fuli Wu,
  • Shihong Liang,
  • Guilin Hu,
  • Defeng Hou,
  • Wenyu Ding,
  • Fucheng Yang

摘要

In response to the monitoring needs of conveyor roller faults in open-pit mines, this paper proposes a fiber optic sensing roller fault diagnosis method based on dynamic model driving. The study first analyzed the coupling mechanism between fiber optic scattering light field and mechanical vibration, and constructed a non-invasive real-time monitoring system. To address the inherent vibration issues caused by equipment structural damage in open-pit mines under extreme weather conditions, a diagnostic method driven by dynamic models is proposed. By constructing a dynamic model of the conveyor system, the mathematical model of the vibration amplitude obtained from theoretical calculations is mixed and analyzed with the light converted vibration signals collected on site. Short time Fourier transform (STFT) is used for signal conversion, and Mel algorithm is used for preprocessing. By combining an improved GoogLeNet LSTM hybrid deep learning model, the preprocessed signals were trained for classification and recognition, and a database of roller fault features was successfully constructed. The experimental results show that the improved DY GougLeNet LSTM model has a testing accuracy of 96.7% in roller fault diagnosis, which is effectively improved compared to the basic model (CNN) with an accuracy of 83.7%. The relevant achievements provide reliable technical support for unmanned operation of open-pit mine conveying systems.