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Research on multitask fault diagnosis and weight visualization of rotating machinery based on convolutional neural network

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Abstract

Recently, deep learning methods such as convolutional neural networks (CNN) have been widely used in fault diagnosis of rotating machinery. However, most methods are not designed to consider the influence of the working conditions and limited to classifying several fault types. In this paper, we propose two CNN-based multitask models, i.e., sigmoid multitask fault diagnosis model and multisoftmax multitask classification model, which can classify the fault types and the working conditions of the signal simultaneously. All samples are jointly learned at the shared network layers, and different types of learning tasks are completed at different subnetwork layers. Results show that the proposed sigmoid multitask fault diagnosis model achieves the overall classification accuracy of 96.8% when testing the CWRU bearing dataset. The proposed multisoftmax multitask classification model is used to classify different working conditions and fault types of planetary gearbox. With frequency signals as inputs, the accuracy rate is able to reach 96.2%, and the classification accuracy of gear and speed conditions reaches more than 99%. Additionally, the gradient-weighted class activation mapping (Grad-CAM) method is used to visualize the weight vectors of different convolutional layers and locate the signal segment of interest to the model.

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Authors and Affiliations

  1. Department of Vehicle Engineering, Academy of Army Armored Forces, Beijing, 100072, China

    Fuzhou Feng, Chunzhi Wu, Junzhen Zhu, Shoujun Wu, Qingwen Tian & Pengcheng Jiang

Authors
  1. Fuzhou Feng
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  2. Chunzhi Wu
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  3. Junzhen Zhu
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  4. Shoujun Wu
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  5. Qingwen Tian
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  6. Pengcheng Jiang
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Correspondence to Fuzhou Feng.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled “Research on multitask fault diagnosis and weight visualization of rotating machinery based on convolutional neural network.”

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Technical Editor: Wallace Moreira Bessa.

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Feng, F., Wu, C., Zhu, J. et al. Research on multitask fault diagnosis and weight visualization of rotating machinery based on convolutional neural network. J Braz. Soc. Mech. Sci. Eng. 42, 603 (2020). https://doi.org/10.1007/s40430-020-02688-6

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  • DOI: https://doi.org/10.1007/s40430-020-02688-6

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