Bearing Intelligent Fault Diagnosis in the Industrial Internet of Things Context: A Lightweight Convolutional Neural Network
The advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCN...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9088980/ |
| _version_ | 1829493655650435072 |
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| author | Yanxin Wang Jing Yan Qifeng Sun Qijian Jiang Yizhi Zhou |
| author_facet | Yanxin Wang Jing Yan Qifeng Sun Qijian Jiang Yizhi Zhou |
| author_sort | Yanxin Wang |
| collection | DOAJ |
| description | The advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCNN) method for intelligent fault diagnosis of bearing, which can largely satisfy the need of less parameter amount and storage space as well as high accuracy. First, depthiwise separable convolution is adopted, and a LCNN structure is constructed through an inverse residual structure and a linear bottleneck layer operation. Secondly, a novel decomposed Hierarchical Search Space is introduced to automatically explore the optimal LCNN for bearing fault diagnosis in the context of the IIoT. In the meantime, the real-time monitoring and fault diagnosis of the model are also deployed. In order to verify the validity of the designed model, Case Western Reserve University Bearing fault dataset and MFPT bearing fault dataset are adopted. The results demonstrate the great advantages of the model. The LCNN model can automatically learn and select the appropriate features, highly improving the fault diagnosis accuracy. Meanwhile, the computational and storage costs of the model are largely reduced, which contributes to its being applied to the mechanical system in the IIoT context. |
| first_indexed | 2024-12-16T06:37:57Z |
| format | Article |
| id | doaj.art-9b38f3ed7d844d138d79c6e65fcce69c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T06:37:57Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9b38f3ed7d844d138d79c6e65fcce69c2022-12-21T22:40:45ZengIEEEIEEE Access2169-35362020-01-018873298734010.1109/ACCESS.2020.29930109088980Bearing Intelligent Fault Diagnosis in the Industrial Internet of Things Context: A Lightweight Convolutional Neural NetworkYanxin Wang0https://orcid.org/0000-0002-4105-7172Jing Yan1Qifeng Sun2Qijian Jiang3Yizhi Zhou4State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an, ChinaState Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an, ChinaSchool of Foreign Studies, Xi’an Jiaotong University, Xi’an, ChinaState Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an, ChinaSchool of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, ChinaThe advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCNN) method for intelligent fault diagnosis of bearing, which can largely satisfy the need of less parameter amount and storage space as well as high accuracy. First, depthiwise separable convolution is adopted, and a LCNN structure is constructed through an inverse residual structure and a linear bottleneck layer operation. Secondly, a novel decomposed Hierarchical Search Space is introduced to automatically explore the optimal LCNN for bearing fault diagnosis in the context of the IIoT. In the meantime, the real-time monitoring and fault diagnosis of the model are also deployed. In order to verify the validity of the designed model, Case Western Reserve University Bearing fault dataset and MFPT bearing fault dataset are adopted. The results demonstrate the great advantages of the model. The LCNN model can automatically learn and select the appropriate features, highly improving the fault diagnosis accuracy. Meanwhile, the computational and storage costs of the model are largely reduced, which contributes to its being applied to the mechanical system in the IIoT context.https://ieeexplore.ieee.org/document/9088980/Fault diagnosislightweight convolutional neural networkdecomposed Hierarchical Search SpaceIndustry 4.0the~industrial Internet of Things |
| spellingShingle | Yanxin Wang Jing Yan Qifeng Sun Qijian Jiang Yizhi Zhou Bearing Intelligent Fault Diagnosis in the Industrial Internet of Things Context: A Lightweight Convolutional Neural Network IEEE Access Fault diagnosis lightweight convolutional neural network decomposed Hierarchical Search Space Industry 4.0 the~industrial Internet of Things |
| title | Bearing Intelligent Fault Diagnosis in the Industrial Internet of Things Context: A Lightweight Convolutional Neural Network |
| title_full | Bearing Intelligent Fault Diagnosis in the Industrial Internet of Things Context: A Lightweight Convolutional Neural Network |
| title_fullStr | Bearing Intelligent Fault Diagnosis in the Industrial Internet of Things Context: A Lightweight Convolutional Neural Network |
| title_full_unstemmed | Bearing Intelligent Fault Diagnosis in the Industrial Internet of Things Context: A Lightweight Convolutional Neural Network |
| title_short | Bearing Intelligent Fault Diagnosis in the Industrial Internet of Things Context: A Lightweight Convolutional Neural Network |
| title_sort | bearing intelligent fault diagnosis in the industrial internet of things context a lightweight convolutional neural network |
| topic | Fault diagnosis lightweight convolutional neural network decomposed Hierarchical Search Space Industry 4.0 the~industrial Internet of Things |
| url | https://ieeexplore.ieee.org/document/9088980/ |
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