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. 2023 Sep 2;13(1):14486.
doi: 10.1038/s41598-023-40369-1.

Fault diagnosis of gearbox based on Fourier Bessel EWT and manifold regularization ELM

Ke Wang  1 , Fengqing Qin  2
Affiliations

Fault diagnosis of gearbox based on Fourier Bessel EWT and manifold regularization ELM

Ke Wang et al. Sci Rep. .

Abstract

The novel fault diagnosis method of gearbox based on Fourier Bessel series expansion-based empirical wavelet transform (FBEWT) and manifold regularization extreme learning machine (MRELM) is proposed to obtain excellent fault diagnosis results of gearbox in this paper. A new feature extraction strategy based on Fourier Bessel series expansion-based empirical wavelet transform is used to capture the key non-stationary features of the vibrational signal of gearbox, and significantly improve the diagnosis ability of gearbox. The ELM with manifold regularization is proposed for fault diagnosis of gearbox. In order to outstand the superiority and stability of the proposed FBEWT and manifold regularization ELM, the balanced dataset and unbalanced dataset, respectively, are used. The experimental results testify that FBEWT-MRELM are more superior and stable than FBEWT-ELM, EWT-MRELM, and EWT-ELM regardless of balanced dataset and unbalanced dataset.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
16 FBIMFs of the signal based on FB-EWT.
Figure 2
Figure 2
The comparison of diagnosis results of gearbox among FBEWT-MRELM, FBEWT-ELM, EWT-MRELM, and EWT-ELM under the conditions of balanced dataset.
Figure 3
Figure 3
The comparison of diagnosis results of gearbox among FBEWT-MRELM, FBEWT-ELM, EWT-MRELM, and EWT-ELM under the conditions of unbalanced dataset.
See this image and copyright information in PMC

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