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A novel intelligent fault diagnosis method based on dual convolutional neural network with multi-level information fusion

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Abstract

Due to the complicacy of mechanical instruments and the noise interference in the working environment, the equipment status information contained in a single sensor is insufficient, and multi-source information contains more complete status information. In order to effectively fuse multi-sensor information and improve the reliability of diagnosis, a multi-level fusion dual convolution neural network (MFDCNN) for fault diagnosis of rotating machinery is proposed in this paper. This approach realizes multi-level fusion of fault information by utilizing the flexibility of the structure of the convolutional neural network. During the training process, the two subnets automatically extract representative features from the multi-sensor timedomain signal and its frequency spectrum in parallel, and then fuse the extracted features for pattern recognition to achieve end-to-end fault diagnosis. Compared with the single sensor diagnosis method and single level information fusion method, this approach has better diagnosis performance.

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Acknowledgments

The studies were funded by the National Natural Science Foundation of China (Grant numbers 51875500 and 61973262), Natural Science Foundation of Hebei Province (Grant number E2020203147), High level talents funding project in Hebei Province (Grant number A201803001) and Project of introducing overseas talents in Hebei Province (C20190516).

Author information

Authors and Affiliations

  1. School of Vehicle and Energy, Yanshan University, Qinhuangdao, Hebei, 066004, China

    Dongying Han

  2. School of Mechanical Engineering, Yanshan University, Qinhuangdao, Hebei, 066004, China

    Dongying Han & Jinghui Tian

  3. School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei, 066004, China

    Peng Xue & Peiming Shi

Authors
  1. Dongying Han
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  2. Jinghui Tian
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  3. Peng Xue
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  4. Peiming Shi
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Corresponding author

Correspondence to Dongying Han.

Additional information

Dongying Han received Ph.D. degree in Mechanical Design and Theory from Yanshan University, Qinhuangdao, China, in 2008. Now she is a Professor in School of Vehicle and Energy of Yanshan University. Her current research interests include fault diagnosis and signal processing.

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Han, D., Tian, J., Xue, P. et al. A novel intelligent fault diagnosis method based on dual convolutional neural network with multi-level information fusion. J Mech Sci Technol 35, 3331–3345 (2021). https://doi.org/10.1007/s12206-021-0707-9

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  • DOI: https://doi.org/10.1007/s12206-021-0707-9

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