A diagnosis method based on graph neural networks embedded with multirelationships of intrinsic mode functions for multiple mechanical faults
Fault diagnosis occupies a pivotal position within the domain of machine and equipment management. Existing methods, however, often exhibit limitations in their scope of application, typically focusing on specific types of signals or faults in individual mechanical components while being constrained...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-08-01
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| Series: | Defence Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214914725001308 |
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| author | Bin Wang Manyi Wang Yadong Xu Liangkuan Wang Shiyu Chen Xuanshi Chen |
| author_facet | Bin Wang Manyi Wang Yadong Xu Liangkuan Wang Shiyu Chen Xuanshi Chen |
| author_sort | Bin Wang |
| collection | DOAJ |
| description | Fault diagnosis occupies a pivotal position within the domain of machine and equipment management. Existing methods, however, often exhibit limitations in their scope of application, typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics. To address the limitations of existing methods, we propose a fault diagnosis method based on graph neural networks (GNNs) embedded with multirelationships of intrinsic mode functions (MIMF). The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions (IMFs) of monitored signals and their multirelationships. Additionally, a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices. Experimental validation with datasets including independent vibration signals for gear fault detection, mixed vibration signals for concurrent gear and bearing faults, and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems. |
| format | Article |
| id | doaj-art-b98e193b2c3b4aec9f34c405fef09240 |
| institution | Kabale University |
| issn | 2214-9147 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Defence Technology |
| spelling | doaj-art-b98e193b2c3b4aec9f34c405fef092402025-08-20T03:36:53ZengKeAi Communications Co., Ltd.Defence Technology2214-91472025-08-015036437310.1016/j.dt.2025.04.014A diagnosis method based on graph neural networks embedded with multirelationships of intrinsic mode functions for multiple mechanical faultsBin Wang0Manyi Wang1Yadong Xu2Liangkuan Wang3Shiyu Chen4Xuanshi Chen5School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China; Corresponding author.School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, ChinaNorthwest Institute of Mechanical and Electrical Engineering, Xianyang, 712099, ChinaWuxi Vocational Institute of Arts & Technology, Wuxi, 214200, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, ChinaFault diagnosis occupies a pivotal position within the domain of machine and equipment management. Existing methods, however, often exhibit limitations in their scope of application, typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics. To address the limitations of existing methods, we propose a fault diagnosis method based on graph neural networks (GNNs) embedded with multirelationships of intrinsic mode functions (MIMF). The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions (IMFs) of monitored signals and their multirelationships. Additionally, a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices. Experimental validation with datasets including independent vibration signals for gear fault detection, mixed vibration signals for concurrent gear and bearing faults, and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.http://www.sciencedirect.com/science/article/pii/S2214914725001308Fault diagnosisGraph neural networksGraph topological structureIntrinsic mode functionsFeature learning |
| spellingShingle | Bin Wang Manyi Wang Yadong Xu Liangkuan Wang Shiyu Chen Xuanshi Chen A diagnosis method based on graph neural networks embedded with multirelationships of intrinsic mode functions for multiple mechanical faults Defence Technology Fault diagnosis Graph neural networks Graph topological structure Intrinsic mode functions Feature learning |
| title | A diagnosis method based on graph neural networks embedded with multirelationships of intrinsic mode functions for multiple mechanical faults |
| title_full | A diagnosis method based on graph neural networks embedded with multirelationships of intrinsic mode functions for multiple mechanical faults |
| title_fullStr | A diagnosis method based on graph neural networks embedded with multirelationships of intrinsic mode functions for multiple mechanical faults |
| title_full_unstemmed | A diagnosis method based on graph neural networks embedded with multirelationships of intrinsic mode functions for multiple mechanical faults |
| title_short | A diagnosis method based on graph neural networks embedded with multirelationships of intrinsic mode functions for multiple mechanical faults |
| title_sort | diagnosis method based on graph neural networks embedded with multirelationships of intrinsic mode functions for multiple mechanical faults |
| topic | Fault diagnosis Graph neural networks Graph topological structure Intrinsic mode functions Feature learning |
| url | http://www.sciencedirect.com/science/article/pii/S2214914725001308 |
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