Discrete wavelet integrated convolutional residual network for bearing fault diagnosis under noise and variable operating conditions
Abstract Bearing faults in rotating machinery can lead to significant economic losses due to downtime and pose serious safety risks. Accurate fault diagnosis is crucial for effective condition monitoring. Traditional methods for diagnosing bearing faults under noisy conditions often rely on complex...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-99346-5 |
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| author | Yunfeng Ni Shuang Li Ping Guo |
| author_facet | Yunfeng Ni Shuang Li Ping Guo |
| author_sort | Yunfeng Ni |
| collection | DOAJ |
| description | Abstract Bearing faults in rotating machinery can lead to significant economic losses due to downtime and pose serious safety risks. Accurate fault diagnosis is crucial for effective condition monitoring. Traditional methods for diagnosing bearing faults under noisy conditions often rely on complex data preprocessing and struggle to maintain accuracy in high-noise environments. To address this challenge, this paper proposes an end-to-end Discrete Wavelet Integrated Convolutional Residual Neural Network (DWCResNet) for bearing fault diagnosis. The model incorporates Discrete Wavelet Transform (DWT) layers to replace traditional downsampling operations in convolutional neural networks, decomposing input signals into low-frequency and high-frequency components to effectively remove high-frequency noise and extract fault features, thereby improving diagnostic performance. The cyclic learning rate strategy enhances training efficiency. Experiments conducted on the Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets demonstrate that DWCResNet achieves higher diagnostic accuracy and noise robustness under various conditions, providing an efficient solution for bearing fault diagnosis in complex noisy environments. |
| format | Article |
| id | doaj-art-73d6727e82da467bb4f90361f1e1b3a2 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-73d6727e82da467bb4f90361f1e1b3a22025-08-20T03:09:35ZengNature PortfolioScientific Reports2045-23222025-05-0115112610.1038/s41598-025-99346-5Discrete wavelet integrated convolutional residual network for bearing fault diagnosis under noise and variable operating conditionsYunfeng Ni0Shuang Li1Ping Guo2Xi’an University Of Science And TechnologyXi’an University Of Science And TechnologyXi’an University Of Science And TechnologyAbstract Bearing faults in rotating machinery can lead to significant economic losses due to downtime and pose serious safety risks. Accurate fault diagnosis is crucial for effective condition monitoring. Traditional methods for diagnosing bearing faults under noisy conditions often rely on complex data preprocessing and struggle to maintain accuracy in high-noise environments. To address this challenge, this paper proposes an end-to-end Discrete Wavelet Integrated Convolutional Residual Neural Network (DWCResNet) for bearing fault diagnosis. The model incorporates Discrete Wavelet Transform (DWT) layers to replace traditional downsampling operations in convolutional neural networks, decomposing input signals into low-frequency and high-frequency components to effectively remove high-frequency noise and extract fault features, thereby improving diagnostic performance. The cyclic learning rate strategy enhances training efficiency. Experiments conducted on the Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets demonstrate that DWCResNet achieves higher diagnostic accuracy and noise robustness under various conditions, providing an efficient solution for bearing fault diagnosis in complex noisy environments.https://doi.org/10.1038/s41598-025-99346-5Rolling bearingFault diagnosisResidual neural networkCycle learning rate strategyWavelet downsamplingDiscrete wavelet transform |
| spellingShingle | Yunfeng Ni Shuang Li Ping Guo Discrete wavelet integrated convolutional residual network for bearing fault diagnosis under noise and variable operating conditions Scientific Reports Rolling bearing Fault diagnosis Residual neural network Cycle learning rate strategy Wavelet downsampling Discrete wavelet transform |
| title | Discrete wavelet integrated convolutional residual network for bearing fault diagnosis under noise and variable operating conditions |
| title_full | Discrete wavelet integrated convolutional residual network for bearing fault diagnosis under noise and variable operating conditions |
| title_fullStr | Discrete wavelet integrated convolutional residual network for bearing fault diagnosis under noise and variable operating conditions |
| title_full_unstemmed | Discrete wavelet integrated convolutional residual network for bearing fault diagnosis under noise and variable operating conditions |
| title_short | Discrete wavelet integrated convolutional residual network for bearing fault diagnosis under noise and variable operating conditions |
| title_sort | discrete wavelet integrated convolutional residual network for bearing fault diagnosis under noise and variable operating conditions |
| topic | Rolling bearing Fault diagnosis Residual neural network Cycle learning rate strategy Wavelet downsampling Discrete wavelet transform |
| url | https://doi.org/10.1038/s41598-025-99346-5 |
| work_keys_str_mv | AT yunfengni discretewaveletintegratedconvolutionalresidualnetworkforbearingfaultdiagnosisundernoiseandvariableoperatingconditions AT shuangli discretewaveletintegratedconvolutionalresidualnetworkforbearingfaultdiagnosisundernoiseandvariableoperatingconditions AT pingguo discretewaveletintegratedconvolutionalresidualnetworkforbearingfaultdiagnosisundernoiseandvariableoperatingconditions |