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Rotating machinery and equipment have a wide range of applications in industry, and they play an extremely important role in industrial production, therefore, fault diagnosis of rotating machinery and equipment is indispensable in industrial manufacturing systems. Numerous fault diagnosis techniques leveraging machine learning and deep learning have emerged, yielding promising results. The paper introduces a novel fault diagnosis approach, integrating a hybrid expert model with convolutional neural networks. Experiments show that this method has good results in diagnosing rotating equipment faults.

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MCNN-Based Model for Rotating Equipment Fault Diagnosis

  • 会议论文

  • 发表日期：

    2025年

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  • Zhenyi Xu,
  • Qilai Wu,
  • Xiaolong Wei,
  • Binkun Liu,
  • Yanming Guo,
  • Yu Kang

摘要

Rotating machinery and equipment have a wide range of applications in industry, and they play an extremely important role in industrial production, therefore, fault diagnosis of rotating machinery and equipment is indispensable in industrial manufacturing systems. Numerous fault diagnosis techniques leveraging machine learning and deep learning have emerged, yielding promising results. The paper introduces a novel fault diagnosis approach, integrating a hybrid expert model with convolutional neural networks. Experiments show that this method has good results in diagnosing rotating equipment faults.