Research on the Rapid Diagnostic Method of Rolling Bearing Fault Based on Cloud–Edge Collaboration
Recent deep-learning methods for fault diagnosis of rolling bearings need a significant amount of computing time and resources. Most of them cannot meet the requirements of real-time fault diagnosis of rolling bearings under the cloud computing framework. This paper proposes a quick cloud–edge colla...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/24/9/1277 |
| _version_ | 1797488680990932992 |
|---|---|
| author | Xianghong Tang Lei Xu Gongsheng Chen |
| author_facet | Xianghong Tang Lei Xu Gongsheng Chen |
| author_sort | Xianghong Tang |
| collection | DOAJ |
| description | Recent deep-learning methods for fault diagnosis of rolling bearings need a significant amount of computing time and resources. Most of them cannot meet the requirements of real-time fault diagnosis of rolling bearings under the cloud computing framework. This paper proposes a quick cloud–edge collaborative bearing fault diagnostic method based on the tradeoff between the advantages and disadvantages of cloud and edge computing. First, a collaborative cloud-based framework and an improved DSCNN–GAP algorithm are suggested to build a general model using the public bearing fault dataset. Second, the general model is distributed to each edge node, and a limited number of unique fault samples acquired by each edge node are used to quickly adjust the parameters of the model before running diagnostic tests. Finally, a fusion result is made from the diagnostic results of each edge node by DS evidence theory. Experiment results show that the proposed method not only improves diagnostic accuracy by DSCNN–GAP and fusion of multi-sensors, but also decreases diagnosis time by migration learning with the cloud–edge collaborative framework. Additionally, the method can effectively enhance data security and privacy protection. |
| first_indexed | 2024-03-10T00:05:47Z |
| format | Article |
| id | doaj.art-e677a760fcdd4a61ad59ef68583fb77d |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T00:05:47Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-e677a760fcdd4a61ad59ef68583fb77d2023-11-23T16:08:57ZengMDPI AGEntropy1099-43002022-09-01249127710.3390/e24091277Research on the Rapid Diagnostic Method of Rolling Bearing Fault Based on Cloud–Edge CollaborationXianghong Tang0Lei Xu1Gongsheng Chen2State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, ChinaKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, ChinaKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, ChinaRecent deep-learning methods for fault diagnosis of rolling bearings need a significant amount of computing time and resources. Most of them cannot meet the requirements of real-time fault diagnosis of rolling bearings under the cloud computing framework. This paper proposes a quick cloud–edge collaborative bearing fault diagnostic method based on the tradeoff between the advantages and disadvantages of cloud and edge computing. First, a collaborative cloud-based framework and an improved DSCNN–GAP algorithm are suggested to build a general model using the public bearing fault dataset. Second, the general model is distributed to each edge node, and a limited number of unique fault samples acquired by each edge node are used to quickly adjust the parameters of the model before running diagnostic tests. Finally, a fusion result is made from the diagnostic results of each edge node by DS evidence theory. Experiment results show that the proposed method not only improves diagnostic accuracy by DSCNN–GAP and fusion of multi-sensors, but also decreases diagnosis time by migration learning with the cloud–edge collaborative framework. Additionally, the method can effectively enhance data security and privacy protection.https://www.mdpi.com/1099-4300/24/9/1277fast diagnosisdepth-separable convolutiontransfer learninginformation fusiondata security |
| spellingShingle | Xianghong Tang Lei Xu Gongsheng Chen Research on the Rapid Diagnostic Method of Rolling Bearing Fault Based on Cloud–Edge Collaboration Entropy fast diagnosis depth-separable convolution transfer learning information fusion data security |
| title | Research on the Rapid Diagnostic Method of Rolling Bearing Fault Based on Cloud–Edge Collaboration |
| title_full | Research on the Rapid Diagnostic Method of Rolling Bearing Fault Based on Cloud–Edge Collaboration |
| title_fullStr | Research on the Rapid Diagnostic Method of Rolling Bearing Fault Based on Cloud–Edge Collaboration |
| title_full_unstemmed | Research on the Rapid Diagnostic Method of Rolling Bearing Fault Based on Cloud–Edge Collaboration |
| title_short | Research on the Rapid Diagnostic Method of Rolling Bearing Fault Based on Cloud–Edge Collaboration |
| title_sort | research on the rapid diagnostic method of rolling bearing fault based on cloud edge collaboration |
| topic | fast diagnosis depth-separable convolution transfer learning information fusion data security |
| url | https://www.mdpi.com/1099-4300/24/9/1277 |
| work_keys_str_mv | AT xianghongtang researchontherapiddiagnosticmethodofrollingbearingfaultbasedoncloudedgecollaboration AT leixu researchontherapiddiagnosticmethodofrollingbearingfaultbasedoncloudedgecollaboration AT gongshengchen researchontherapiddiagnosticmethodofrollingbearingfaultbasedoncloudedgecollaboration |