Multi-Condition Intelligent Fault Diagnosis Based on Tree-Structured Labels and Hierarchical Multi-Granularity Diagnostic Network
The aim of this study is to improve the cross-condition domain adaptability of bearing fault diagnosis models and their diagnostic performance under previously unknown conditions. Thus, this paper proposes a multi-condition adaptive bearing fault diagnosis method based on multi-granularity data anno...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/12/12/891 |
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| author | Hehua Yan Jinbiao Tan Yixiong Luo Shiyong Wang Jiafu Wan |
| author_facet | Hehua Yan Jinbiao Tan Yixiong Luo Shiyong Wang Jiafu Wan |
| author_sort | Hehua Yan |
| collection | DOAJ |
| description | The aim of this study is to improve the cross-condition domain adaptability of bearing fault diagnosis models and their diagnostic performance under previously unknown conditions. Thus, this paper proposes a multi-condition adaptive bearing fault diagnosis method based on multi-granularity data annotation. A tree-structured labeling scheme is introduced to allow for multi-granularity fault annotation. A hierarchical multi-granularity diagnostic network is designed to automatically learn multi-level fault information from condition data using feature extractors of varying granularity, allowing for the extraction of shared fault information across conditions. Additionally, a multi-granularity fault loss function is developed to help the deep network learn tree-structured labels, improving intra-class compactness and reducing hierarchical similarity between classes. Two experimental cases demonstrate that the proposed method exhibits robust cross-condition domain adaptability and performs better in unseen conditions than state-of-the-art methods. |
| format | Article |
| id | doaj-art-1ef3de9ca6ae4638a0564b19da625fc7 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-1ef3de9ca6ae4638a0564b19da625fc72024-12-27T14:37:05ZengMDPI AGMachines2075-17022024-12-01121289110.3390/machines12120891Multi-Condition Intelligent Fault Diagnosis Based on Tree-Structured Labels and Hierarchical Multi-Granularity Diagnostic NetworkHehua Yan0Jinbiao Tan1Yixiong Luo2Shiyong Wang3Jiafu Wan4The School of Electrical Technology, Guangdong Mechanical and Electrical Polytechnic, Guangzhou 510515, ChinaThe School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, ChinaThe School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, ChinaThe School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, ChinaThe School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, ChinaThe aim of this study is to improve the cross-condition domain adaptability of bearing fault diagnosis models and their diagnostic performance under previously unknown conditions. Thus, this paper proposes a multi-condition adaptive bearing fault diagnosis method based on multi-granularity data annotation. A tree-structured labeling scheme is introduced to allow for multi-granularity fault annotation. A hierarchical multi-granularity diagnostic network is designed to automatically learn multi-level fault information from condition data using feature extractors of varying granularity, allowing for the extraction of shared fault information across conditions. Additionally, a multi-granularity fault loss function is developed to help the deep network learn tree-structured labels, improving intra-class compactness and reducing hierarchical similarity between classes. Two experimental cases demonstrate that the proposed method exhibits robust cross-condition domain adaptability and performs better in unseen conditions than state-of-the-art methods.https://www.mdpi.com/2075-1702/12/12/891deep learningdata annotationfault diagnosismultilevel labelneural network |
| spellingShingle | Hehua Yan Jinbiao Tan Yixiong Luo Shiyong Wang Jiafu Wan Multi-Condition Intelligent Fault Diagnosis Based on Tree-Structured Labels and Hierarchical Multi-Granularity Diagnostic Network Machines deep learning data annotation fault diagnosis multilevel label neural network |
| title | Multi-Condition Intelligent Fault Diagnosis Based on Tree-Structured Labels and Hierarchical Multi-Granularity Diagnostic Network |
| title_full | Multi-Condition Intelligent Fault Diagnosis Based on Tree-Structured Labels and Hierarchical Multi-Granularity Diagnostic Network |
| title_fullStr | Multi-Condition Intelligent Fault Diagnosis Based on Tree-Structured Labels and Hierarchical Multi-Granularity Diagnostic Network |
| title_full_unstemmed | Multi-Condition Intelligent Fault Diagnosis Based on Tree-Structured Labels and Hierarchical Multi-Granularity Diagnostic Network |
| title_short | Multi-Condition Intelligent Fault Diagnosis Based on Tree-Structured Labels and Hierarchical Multi-Granularity Diagnostic Network |
| title_sort | multi condition intelligent fault diagnosis based on tree structured labels and hierarchical multi granularity diagnostic network |
| topic | deep learning data annotation fault diagnosis multilevel label neural network |
| url | https://www.mdpi.com/2075-1702/12/12/891 |
| work_keys_str_mv | AT hehuayan multiconditionintelligentfaultdiagnosisbasedontreestructuredlabelsandhierarchicalmultigranularitydiagnosticnetwork AT jinbiaotan multiconditionintelligentfaultdiagnosisbasedontreestructuredlabelsandhierarchicalmultigranularitydiagnosticnetwork AT yixiongluo multiconditionintelligentfaultdiagnosisbasedontreestructuredlabelsandhierarchicalmultigranularitydiagnosticnetwork AT shiyongwang multiconditionintelligentfaultdiagnosisbasedontreestructuredlabelsandhierarchicalmultigranularitydiagnosticnetwork AT jiafuwan multiconditionintelligentfaultdiagnosisbasedontreestructuredlabelsandhierarchicalmultigranularitydiagnosticnetwork |