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Fault Diagnosis of Rolling Bearings Based on the Improved Symmetrized Dot Pattern Enhanced Convolutional Neural Networks

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Abstract

Purpose

The main purpose of this paper is to change the structure of the SDP to include more fault information. Furthermore, improve the diagnostic accuracy and anti-noise performance of the bearing fault diagnosis method based on SDP.

Methods

First, a multi-interval asymmetric dot pattern (MADP) is proposed by modifying the expression of SDP. Then, the improved SDP (multi-modal multi-interval asymmetric dot pattern, MMADP) is established by the MADP method fused with the multiple effective IMF components which are obtained through CEEMDAN decomposition. Finally, a bearing fault diagnosis model is established based on MMADP and convolutional neural network.

Results

The effectiveness of the proposed fault diagnosis method is validated on the CWRU dataset. The results indicate that under Gaussian white noise with a signal-to-noise ratio (SNR) of above 4 dB and − 6 dB, the accuracy of the proposed fault diagnosis method reaches 100 and 93.3%, respectively.

Conclusion

In this paper, a method (MADP) transforming time series signals into images is proposed, and a method for fault diagnosis of rolling bearings is formed through combination of CEEMDAN and CNN. The bearing fault diagnosis method has good anti-noise performance, and the MADP has potential value in the processing of sound signals.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The research work was co-supported by the National Natural Science Foundation of China (51875014, 51620105010, 51575019) and China Postdoctoral Science Foundation (2021M700329).

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Authors and Affiliations

  1. School of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Bei**g, 100191, China

    ** Liu, Jian Shi & Shao** Wang

  2. Key Laboratory of Advanced Forging and Stam** Technology and Science of Ministry of Education of China, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Lijian **-Liu-Aff1">** Liu

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  5. Lijie Zhang
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Correspondence to Shao** Wang.

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Conflict of Interest No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part.

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Liu, X., **a, L., Shi, J. et al. Fault Diagnosis of Rolling Bearings Based on the Improved Symmetrized Dot Pattern Enhanced Convolutional Neural Networks. J. Vib. Eng. Technol. 12, 1897–1908 (2024). https://doi.org/10.1007/s42417-023-00949-x

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