Fault Diagnosis of Rolling Bearings using Multi-scale Convolution Neural Network with Hybrid Attention Mechanism
编号:71 访问权限:仅限参会人 更新:2022-12-22 22:29:44 浏览:367次 张贴报告

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摘要
 
With the continuous development of artificial intelligence technology, mechanical fault diagnosis methods based on deep learning (DL) have made great progress. Nevertheless, the operating conditions of mechanical equipment are subject to substantial random factors in real industrial scenarios and there are different levels of environmental noise. This fact undoubtedly puts forward higher requirements for the adaptability and robustness of the model. In this paper, a multi-scale convolution neural network with hybrid attention mechanism (MSCNN-HAM) is proposed to solve the above issues. First, to extract multiscale features and filter invalid information, the one-dimensional vibration signal is input into the multiscale feature learning module. Second, a hybrid attention module is introduced to obtain more effective features. Third, the deep feature is extracted by the module including a series of small convolution kernels. Finally, fault diagnosis is realized through a classifier. The designed method is tested on experiments with different levels of environmental noise, and the final result proved its effectiveness and superiority.
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报告人
Feiyu Tian
Xi

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重要日期
  • 会议日期

    11月30日

    2022

    12月02日

    2022

  • 11月30日 2022

    初稿截稿日期

  • 12月24日 2022

    报告提交截止日期

  • 04月13日 2023

    注册截止日期

主办单位
Harbin Insititute of Technology
China Instrument and Control Society
Heilongjiang Instrument and Control Society
Chinese Institute of Electronics
IEEE I&M Society Harbin Chapter
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