JOURNAL OF LIGHT INDUSTRY

CN 41-1437/TS  ISSN 2096-1553

Volume 35 Issue 1
January 2020
Article Contents
DING Di and NAN Guofang. Application of CNN-RNN fusion method in fault diagnosis of rotating machinery[J]. Journal of Light Industry, 2020, 35(1): 102-108. doi: 10.12187/2020.01.013
Citation: DING Di and NAN Guofang. Application of CNN-RNN fusion method in fault diagnosis of rotating machinery[J]. Journal of Light Industry, 2020, 35(1): 102-108. doi: 10.12187/2020.01.013 shu

Application of CNN-RNN fusion method in fault diagnosis of rotating machinery

  • Received Date: 2019-08-15
  • Aiming at the problems of current fault diagnosis of rotating machinery with long calculation time and low accuracy, a CNN-RNN fusion analysis method was proposed by combining the feature extraction capability of CNN and the processing capability of RNN timing. A one-dimensional CNN network was used to extract feature data, which removed invalid information affected by environmental noise and other factors and still had timeliness. Then, the RNN with high accuracy of processing time-series data calculated the feature data and then applied to the fault diagnosis of rotating machinery. The experimental results on the test set showed that the method did not require manual extraction of feature data, the computing time was reduced by about 1/2, and the accuracy of fault diagnosis was increased by about 2%.This method had feasibility.
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