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Application of Multiscale Learning Neural Network Based on CNN in Bearing Fault Diagnosis

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A Correction to this article was published on 19 November 2019

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Abstract

With the application of intelligent manufacturing becoming more and more widely, the losses caused by mechanical faults of equipment increase. Identifying and troubleshooting faults in an early stage are important. The process of traditional data-driven fault diagnosis method includes data acquisition, fault classification, and feature extraction, in which classification accuracy is directly affected by the result of feature extraction. As a common deep learning method in image recognition, the convolutional neural network (CNN) demonstrates good performance in fault diagnosis. CNN can adaptively extract features from original signals and eliminate the effect of conventional handcrafted features. In this study, a multiscale learning neural network that contains one-dimension (1D) and two-dimension (2D) convolution channels is proposed. The network can learn the local correlation of adjacent and nonadjacent intervals in periodic signals, such as vibration data. The Paderborn data set is came into use to demonstrate the classification accuracy of the method which is brought forward, which includes three conditions of healthy, outer ring (OR) damage and inner ring (IR) damage. The classification accuracy of the method which is put forward is up to 98.58%. The same dataset was applied to test the classification accuracy of support vector machine (SVM) for comparison. And the proposed multiscale learning neural network demonstrates considerable improvements.

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Change history

  • 19 November 2019

    The Publisher regrets an error on the printed front cover of the October 2019 issue. The issue numbers were incorrectly listed as Volume 91, Nos. 10-12, October 2019. The correct number should be: "Volume 91, No. 10, October 2019"

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Authors and Affiliations

  1. Shandong University, Qingdao, 266237, Shandong, China

    Daichao Wang, Qingwen Guo, Yan Song & Yibin Li

  2. China Naval Academy, Beijing, 100161, China

    Shengyao Gao

Authors
  1. Daichao Wang
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  2. Qingwen Guo
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  3. Yan Song
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  4. Shengyao Gao
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  5. Yibin Li
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Correspondence to Yibin Li.

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Wang, D., Guo, Q., Song, Y. et al. Application of Multiscale Learning Neural Network Based on CNN in Bearing Fault Diagnosis. J Sign Process Syst 91, 1205–1217 (2019). https://doi.org/10.1007/s11265-019-01461-w

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  • DOI: https://doi.org/10.1007/s11265-019-01461-w

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