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Fault diagnosis method based on EWT and improved ConvNeXt networks.

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Title: Fault diagnosis method based on EWT and improved ConvNeXt networks.
Authors: LI, Jun1 lijun691201@mail.lzjtu.cn, MA, Yi1
Source: Journal of Measurement Science & Instrumentation. Jun2026, Vol. 17 Issue 2, p307-319. 13p.
Subjects: Fault diagnosis, Wavelet transforms, Time-frequency analysis, Artificial neural networks, Noise control, Attention control
Abstract: Due to the interference of strong noise, feature extraction faces the challenge of limited information, which is not conducive to motor equipment fault diagnosis. This paper proposes a fault diagnosis method based on the empirical wavelet transform (EWT) and an improved ConvNeXt network. The modal components were extracted from the signals of different sensors using empirical wavelet transform, noise was removed, and then the signals were reconstructed. Secondly, the short-time Fourier transform (STFT) was used to convert the one-dimensional signal after noise reduction and reconstruction into a two-dimensional time-frequency spectrum image that enhanced signal features. Single-channel images generated by a single sensor were fused to form multi-channel images, thereby boosting the feature extraction capability of the ConvNeXt network. Additionally, the Ghost convolution module and the efficient local attention mechanism (ELA) were introduced into the ConvNeXt-T (ConvNeXt-Tiny) network, further enhancing the network's performance. Experimental validation was conducted on application examples of various fault diagnostic devices, and comparisons were made with existing mainstream deep learning methods such as SE-InceptionV3, CBAM-ResNet, and CNN-LSTM etc. Experimental results confirmed under different noise environments and variable operating conditions, the proposed method achieved better diagnostic accuracy and enhanced generalization performance. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Fault diagnosis method based on EWT and improved ConvNeXt networks.
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  Data: <searchLink fieldCode="AR" term="%22LI%2C+Jun%22">LI, Jun</searchLink><relatesTo>1</relatesTo><i> lijun691201@mail.lzjtu.cn</i><br /><searchLink fieldCode="AR" term="%22MA%2C+Yi%22">MA, Yi</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Wavelet+transforms%22">Wavelet transforms</searchLink><br /><searchLink fieldCode="DE" term="%22Time-frequency+analysis%22">Time-frequency analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Noise+control%22">Noise control</searchLink><br /><searchLink fieldCode="DE" term="%22Attention+control%22">Attention control</searchLink>
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  Data: Due to the interference of strong noise, feature extraction faces the challenge of limited information, which is not conducive to motor equipment fault diagnosis. This paper proposes a fault diagnosis method based on the empirical wavelet transform (EWT) and an improved ConvNeXt network. The modal components were extracted from the signals of different sensors using empirical wavelet transform, noise was removed, and then the signals were reconstructed. Secondly, the short-time Fourier transform (STFT) was used to convert the one-dimensional signal after noise reduction and reconstruction into a two-dimensional time-frequency spectrum image that enhanced signal features. Single-channel images generated by a single sensor were fused to form multi-channel images, thereby boosting the feature extraction capability of the ConvNeXt network. Additionally, the Ghost convolution module and the efficient local attention mechanism (ELA) were introduced into the ConvNeXt-T (ConvNeXt-Tiny) network, further enhancing the network's performance. Experimental validation was conducted on application examples of various fault diagnostic devices, and comparisons were made with existing mainstream deep learning methods such as SE-InceptionV3, CBAM-ResNet, and CNN-LSTM etc. Experimental results confirmed under different noise environments and variable operating conditions, the proposed method achieved better diagnostic accuracy and enhanced generalization performance. [ABSTRACT FROM AUTHOR]
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  Group: Ab
  Data: <i>Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.62756/jmsi.1674-8042.2026026
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 307
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      – SubjectFull: Fault diagnosis
        Type: general
      – SubjectFull: Wavelet transforms
        Type: general
      – SubjectFull: Time-frequency analysis
        Type: general
      – SubjectFull: Artificial neural networks
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      – SubjectFull: Noise control
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      – SubjectFull: Attention control
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              Text: Jun2026
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              Y: 2026
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