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A small sample bearing fault diagnosis method based on variational mode decomposition, autocorrelation function, and convolutional neural network

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Abstract

Bearing fault is a factor that directly affects the reliability of the machine tools. Small sample bearing fault diagnosis plays an important role to improve the reliability of machine tools. However, the over-fitting and weak performance are common problems of small sample bearing fault diagnoses based on deep learning. This paper proposed a different method based on data enhancement and convolutional neural networks (CNN). The method firstly decomposes the vibration signals of the rolling bearing according to the optimal decomposition criterion of variational mode decomposition (VMD). Then, it selects the modes according to the fault frequency characteristics and filters the selected modes into multiple sub-band signals by band-pass filters. Moreover, it computes out the autocorrelation peak vector of the sub-band signals. Finally, the method uses the fault diagnosis network made from a 4-layer neural network, automatically extracts bearing fault features, and predicts the fault types of the testing signals. The experiment shows that the proposed method has a 99% accuracy rate in the rolling bearing fault data set XJTU-SY and requires fewer training samples than the latest methods of NKH-KELM and VMD-CNN. The proposed method has high accuracy under the small sample conditions, which makes it applicable in some practical CNC machine tools with difficulties obtaining bearing samples.

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Data availability

The implementation and data sets used in this paper are available from the corresponding author upon request.

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Funding

This work was supported by the growth project of young scientific and technological talents in Guizhou of China for colleges and universities [grant number Qian Jiao He KY2020 137, KY 2020 142], the tripartite joint fund project of Guizhou Provincial Departmen t Science and Technology [grant number LH2016 7275], the national natural science foundation of China [grant number 61762001], and creative research groups of the natural science foundation of Guizhou of china [grant Qian Jiao He KY Zi 2019069 and 2018034].

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Authors and Affiliations

  1. Anshun University, Anshun, 561000, Guizhou, China

    Yuhui Wu, Licai Liu & Shuqu Qian

  2. Saint Paul University Philippines, 3500, Tuguegarao City, Cagayan, Philippines

    Yuhui Wu

Authors
  1. Yuhui Wu
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  2. Licai Liu
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  3. Shuqu Qian
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Contributions

Yuhui Wu contributes to the investigation, methodology, writing, and editing.

Licai Liu contributes to the conceptualization, formal analysis, and investigation.

Shuqu Qian contributes to the data curation, validation, and writing.

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Correspondence to Yuhui Wu.

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Wu, Y., Liu, L. & Qian, S. A small sample bearing fault diagnosis method based on variational mode decomposition, autocorrelation function, and convolutional neural network. Int J Adv Manuf Technol 124, 3887–3898 (2023). https://doi.org/10.1007/s00170-021-08126-8

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