Prior Knowledge-Informed Graph Neural Network with Multi-Source Data-Weighted Fusion for Intelligent Bogie Fault Diagnosis
The current multi-source fusion fault diagnosis algorithm rarely considers the information correlation of multi-sensor networks and the important difference between multi-sensors. Aiming at this challenge, we propose an intelligent fault identification method for high-speed railway bogie based on th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/12/12/838 |
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| author | Yuanxing Huang Bofeng Cui Xianqun Mao Jinsong Yang |
| author_facet | Yuanxing Huang Bofeng Cui Xianqun Mao Jinsong Yang |
| author_sort | Yuanxing Huang |
| collection | DOAJ |
| description | The current multi-source fusion fault diagnosis algorithm rarely considers the information correlation of multi-sensor networks and the important difference between multi-sensors. Aiming at this challenge, we propose an intelligent fault identification method for high-speed railway bogie based on the graph neural network embedded with prior knowledge, which brings the spatial information of the sensor network into the diagnosis algorithm and re-weights each sensor according to the diagnosis results. Firstly, the time–domain correlation of vibration signals between bogie sensor networks is calculated as the prior knowledge. Then, based on the spatial topological relationship of the sensors, the graph correlation matrix of the network is established. Further, the importance of each sensor is dynamically analyzed and updated together with the training process. The proposed method is tested on a high-precision bogie test bed, and the experimental results demonstrate the effectiveness and superiority of the proposed method. |
| format | Article |
| id | doaj-art-0a6c6dd94d624c3cb0d33fc6479d9bbd |
| institution | OA Journals |
| issn | 2075-1702 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-0a6c6dd94d624c3cb0d33fc6479d9bbd2025-08-20T02:00:39ZengMDPI AGMachines2075-17022024-11-01121283810.3390/machines12120838Prior Knowledge-Informed Graph Neural Network with Multi-Source Data-Weighted Fusion for Intelligent Bogie Fault DiagnosisYuanxing Huang0Bofeng Cui1Xianqun Mao2Jinsong Yang3Marine Design and Research Institute of China, Shanghai 200011, ChinaMarine Design and Research Institute of China, Shanghai 200011, ChinaMarine Design and Research Institute of China, Shanghai 200011, ChinaSchool of Traffic & Transportation Engineering, Central South University, Changsha 410004, ChinaThe current multi-source fusion fault diagnosis algorithm rarely considers the information correlation of multi-sensor networks and the important difference between multi-sensors. Aiming at this challenge, we propose an intelligent fault identification method for high-speed railway bogie based on the graph neural network embedded with prior knowledge, which brings the spatial information of the sensor network into the diagnosis algorithm and re-weights each sensor according to the diagnosis results. Firstly, the time–domain correlation of vibration signals between bogie sensor networks is calculated as the prior knowledge. Then, based on the spatial topological relationship of the sensors, the graph correlation matrix of the network is established. Further, the importance of each sensor is dynamically analyzed and updated together with the training process. The proposed method is tested on a high-precision bogie test bed, and the experimental results demonstrate the effectiveness and superiority of the proposed method.https://www.mdpi.com/2075-1702/12/12/838fault diagnosisdeep learningbogiegraph neural network |
| spellingShingle | Yuanxing Huang Bofeng Cui Xianqun Mao Jinsong Yang Prior Knowledge-Informed Graph Neural Network with Multi-Source Data-Weighted Fusion for Intelligent Bogie Fault Diagnosis Machines fault diagnosis deep learning bogie graph neural network |
| title | Prior Knowledge-Informed Graph Neural Network with Multi-Source Data-Weighted Fusion for Intelligent Bogie Fault Diagnosis |
| title_full | Prior Knowledge-Informed Graph Neural Network with Multi-Source Data-Weighted Fusion for Intelligent Bogie Fault Diagnosis |
| title_fullStr | Prior Knowledge-Informed Graph Neural Network with Multi-Source Data-Weighted Fusion for Intelligent Bogie Fault Diagnosis |
| title_full_unstemmed | Prior Knowledge-Informed Graph Neural Network with Multi-Source Data-Weighted Fusion for Intelligent Bogie Fault Diagnosis |
| title_short | Prior Knowledge-Informed Graph Neural Network with Multi-Source Data-Weighted Fusion for Intelligent Bogie Fault Diagnosis |
| title_sort | prior knowledge informed graph neural network with multi source data weighted fusion for intelligent bogie fault diagnosis |
| topic | fault diagnosis deep learning bogie graph neural network |
| url | https://www.mdpi.com/2075-1702/12/12/838 |
| work_keys_str_mv | AT yuanxinghuang priorknowledgeinformedgraphneuralnetworkwithmultisourcedataweightedfusionforintelligentbogiefaultdiagnosis AT bofengcui priorknowledgeinformedgraphneuralnetworkwithmultisourcedataweightedfusionforintelligentbogiefaultdiagnosis AT xianqunmao priorknowledgeinformedgraphneuralnetworkwithmultisourcedataweightedfusionforintelligentbogiefaultdiagnosis AT jinsongyang priorknowledgeinformedgraphneuralnetworkwithmultisourcedataweightedfusionforintelligentbogiefaultdiagnosis |