MTAGCN: Multi-Task Graph-Guided Convolutional Network with Attention Mechanism for Intelligent Fault Diagnosis of Rotating Machinery
Deep learning (DL)-based methods have shown great success in multi-category fault diagnosis due to their hierarchical networks and automatic feature extraction. However, their superior performance is mostly based on single-task learning, which makes them unsuitable for increasingly sophisticated eng...
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MDPI AG
2025-04-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/5/347 |
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| author | Bo Wang Shuai Zhao |
| author_facet | Bo Wang Shuai Zhao |
| author_sort | Bo Wang |
| collection | DOAJ |
| description | Deep learning (DL)-based methods have shown great success in multi-category fault diagnosis due to their hierarchical networks and automatic feature extraction. However, their superior performance is mostly based on single-task learning, which makes them unsuitable for increasingly sophisticated engineering environments. In this paper, a novel multi-task graph-guided convolutional network with an attention mechanism for intelligent fault diagnosis, named MTAGCN, is proposed. Most existing fault diagnosis models are commonly bounded by a single diagnosis objective, especially when handling multiple tasks jointly. To address this limitation, a new multi-task fault diagnosis framework is designed, incorporating an attention mechanism between the task-specific module and task-shared modules. This framework enables multiple related tasks to be learned jointly while improving diagnostic and identification performance. Moreover, it is observed that most existing DL-based methods share incomplete fault representations, leading to unsatisfactory fault diagnosis. To overcome this issue, a graph convolutional network (GCN)-based fault diagnosis framework is introduced, which not only captures structural characteristics but also enhances diagnostic effectiveness. Comprehensive experiments based on three case studies demonstrate that the proposed MTAGCN outperforms state-of-the-art (SOTA) methods, striking a good balance between accuracy and multi-task learning. |
| format | Article |
| id | doaj-art-d9bb6adf10c54a6491191db4a23a41ec |
| institution | OA Journals |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-d9bb6adf10c54a6491191db4a23a41ec2025-08-20T01:56:19ZengMDPI AGMachines2075-17022025-04-0113534710.3390/machines13050347MTAGCN: Multi-Task Graph-Guided Convolutional Network with Attention Mechanism for Intelligent Fault Diagnosis of Rotating MachineryBo Wang0Shuai Zhao1School of Information and Artificial Intelligence, Nanchang Institute of Science & Technology, Nanchang 330108, ChinaSchool of Information and Artificial Intelligence, Nanchang Institute of Science & Technology, Nanchang 330108, ChinaDeep learning (DL)-based methods have shown great success in multi-category fault diagnosis due to their hierarchical networks and automatic feature extraction. However, their superior performance is mostly based on single-task learning, which makes them unsuitable for increasingly sophisticated engineering environments. In this paper, a novel multi-task graph-guided convolutional network with an attention mechanism for intelligent fault diagnosis, named MTAGCN, is proposed. Most existing fault diagnosis models are commonly bounded by a single diagnosis objective, especially when handling multiple tasks jointly. To address this limitation, a new multi-task fault diagnosis framework is designed, incorporating an attention mechanism between the task-specific module and task-shared modules. This framework enables multiple related tasks to be learned jointly while improving diagnostic and identification performance. Moreover, it is observed that most existing DL-based methods share incomplete fault representations, leading to unsatisfactory fault diagnosis. To overcome this issue, a graph convolutional network (GCN)-based fault diagnosis framework is introduced, which not only captures structural characteristics but also enhances diagnostic effectiveness. Comprehensive experiments based on three case studies demonstrate that the proposed MTAGCN outperforms state-of-the-art (SOTA) methods, striking a good balance between accuracy and multi-task learning.https://www.mdpi.com/2075-1702/13/5/347fault diagnosismulti-task learninggraph convolutional networksattention mechanism |
| spellingShingle | Bo Wang Shuai Zhao MTAGCN: Multi-Task Graph-Guided Convolutional Network with Attention Mechanism for Intelligent Fault Diagnosis of Rotating Machinery Machines fault diagnosis multi-task learning graph convolutional networks attention mechanism |
| title | MTAGCN: Multi-Task Graph-Guided Convolutional Network with Attention Mechanism for Intelligent Fault Diagnosis of Rotating Machinery |
| title_full | MTAGCN: Multi-Task Graph-Guided Convolutional Network with Attention Mechanism for Intelligent Fault Diagnosis of Rotating Machinery |
| title_fullStr | MTAGCN: Multi-Task Graph-Guided Convolutional Network with Attention Mechanism for Intelligent Fault Diagnosis of Rotating Machinery |
| title_full_unstemmed | MTAGCN: Multi-Task Graph-Guided Convolutional Network with Attention Mechanism for Intelligent Fault Diagnosis of Rotating Machinery |
| title_short | MTAGCN: Multi-Task Graph-Guided Convolutional Network with Attention Mechanism for Intelligent Fault Diagnosis of Rotating Machinery |
| title_sort | mtagcn multi task graph guided convolutional network with attention mechanism for intelligent fault diagnosis of rotating machinery |
| topic | fault diagnosis multi-task learning graph convolutional networks attention mechanism |
| url | https://www.mdpi.com/2075-1702/13/5/347 |
| work_keys_str_mv | AT bowang mtagcnmultitaskgraphguidedconvolutionalnetworkwithattentionmechanismforintelligentfaultdiagnosisofrotatingmachinery AT shuaizhao mtagcnmultitaskgraphguidedconvolutionalnetworkwithattentionmechanismforintelligentfaultdiagnosisofrotatingmachinery |