Rolling bearing fault identification with acoustic emission signal based on variable-pooling multiscale convolutional neural networks
Abstract This paper propose a new fault identification method based on variable pooling multiscale CNN (VPMCNN), which solves the bearing industrial problem of huge variable features and inherent multiscale characteristics in acoustic emission (AE) signals. First, the pooling projection components (...
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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/s41598-025-00573-7 |
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| author | Yue Zhang Yang Yu Zheng Yang Qiang Liu |
| author_facet | Yue Zhang Yang Yu Zheng Yang Qiang Liu |
| author_sort | Yue Zhang |
| collection | DOAJ |
| description | Abstract This paper propose a new fault identification method based on variable pooling multiscale CNN (VPMCNN), which solves the bearing industrial problem of huge variable features and inherent multiscale characteristics in acoustic emission (AE) signals. First, the pooling projection components (PPCs) of the signals are obtained through the variable pooling layer. The PPCs consider the curse of invariant feature weight in traditional CNN pooling layer, and select the more weighted features to enhance the classifying quality. Second, an improved multiscale fusion feature module is introduced to further extract the hidden features, which is called fused components (FCs). The FCs aims to automatically extract multiple scale features using different filter sizes from raw acoustic signals. Then the GAP (Global Average Pooling) layer is performed to realize classification. Finally, the fault identification using the proposed algorithm is performed by testing the bearing AE signals with single operating condition and variable operating conditions, and the results show the effectiveness of the proposed method, compared with existing AE based bearing fault identification methods. |
| format | Article |
| id | doaj-art-8b442aef1d2e4a7c807a2d1cd89156f8 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8b442aef1d2e4a7c807a2d1cd89156f82025-08-20T02:15:15ZengNature PortfolioScientific Reports2045-23222025-05-0115111610.1038/s41598-025-00573-7Rolling bearing fault identification with acoustic emission signal based on variable-pooling multiscale convolutional neural networksYue Zhang0Yang Yu1Zheng Yang2Qiang Liu3College of Information Science and Engineering, Shenyang University of TechnologyCollege of Information Science and Engineering, Shenyang University of TechnologyCollege of Information Science and Engineering, Shenyang University of TechnologyCollege of Information Science and Engineering, Shenyang University of TechnologyAbstract This paper propose a new fault identification method based on variable pooling multiscale CNN (VPMCNN), which solves the bearing industrial problem of huge variable features and inherent multiscale characteristics in acoustic emission (AE) signals. First, the pooling projection components (PPCs) of the signals are obtained through the variable pooling layer. The PPCs consider the curse of invariant feature weight in traditional CNN pooling layer, and select the more weighted features to enhance the classifying quality. Second, an improved multiscale fusion feature module is introduced to further extract the hidden features, which is called fused components (FCs). The FCs aims to automatically extract multiple scale features using different filter sizes from raw acoustic signals. Then the GAP (Global Average Pooling) layer is performed to realize classification. Finally, the fault identification using the proposed algorithm is performed by testing the bearing AE signals with single operating condition and variable operating conditions, and the results show the effectiveness of the proposed method, compared with existing AE based bearing fault identification methods.https://doi.org/10.1038/s41598-025-00573-7Convolutional neural networkFault IdentificationMultiscale FusionAcoustic emissionPooling projection components, Variable operating conditions |
| spellingShingle | Yue Zhang Yang Yu Zheng Yang Qiang Liu Rolling bearing fault identification with acoustic emission signal based on variable-pooling multiscale convolutional neural networks Scientific Reports Convolutional neural network Fault Identification Multiscale Fusion Acoustic emission Pooling projection components, Variable operating conditions |
| title | Rolling bearing fault identification with acoustic emission signal based on variable-pooling multiscale convolutional neural networks |
| title_full | Rolling bearing fault identification with acoustic emission signal based on variable-pooling multiscale convolutional neural networks |
| title_fullStr | Rolling bearing fault identification with acoustic emission signal based on variable-pooling multiscale convolutional neural networks |
| title_full_unstemmed | Rolling bearing fault identification with acoustic emission signal based on variable-pooling multiscale convolutional neural networks |
| title_short | Rolling bearing fault identification with acoustic emission signal based on variable-pooling multiscale convolutional neural networks |
| title_sort | rolling bearing fault identification with acoustic emission signal based on variable pooling multiscale convolutional neural networks |
| topic | Convolutional neural network Fault Identification Multiscale Fusion Acoustic emission Pooling projection components, Variable operating conditions |
| url | https://doi.org/10.1038/s41598-025-00573-7 |
| work_keys_str_mv | AT yuezhang rollingbearingfaultidentificationwithacousticemissionsignalbasedonvariablepoolingmultiscaleconvolutionalneuralnetworks AT yangyu rollingbearingfaultidentificationwithacousticemissionsignalbasedonvariablepoolingmultiscaleconvolutionalneuralnetworks AT zhengyang rollingbearingfaultidentificationwithacousticemissionsignalbasedonvariablepoolingmultiscaleconvolutionalneuralnetworks AT qiangliu rollingbearingfaultidentificationwithacousticemissionsignalbasedonvariablepoolingmultiscaleconvolutionalneuralnetworks |