Graph Feature Fusion-Driven Fault Diagnosis of Complex Process Industrial System Based on Multivariate Heterogeneous Data
The stable operation of the process industrial system, which is integrated with various complex equipment, is the premise of production, which requires the condition monitoring and diagnosis of the system. Recently, the continuous development of deep learning (DL) has promoted the research of intell...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2024/9197578 |
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| _version_ | 1850224104608628736 |
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| author | Fengyuan Zhang Jie Liu Xiang Lu Tao Li Yi Li Yongji Sheng Hu Wang Yingwei Liu |
| author_facet | Fengyuan Zhang Jie Liu Xiang Lu Tao Li Yi Li Yongji Sheng Hu Wang Yingwei Liu |
| author_sort | Fengyuan Zhang |
| collection | DOAJ |
| description | The stable operation of the process industrial system, which is integrated with various complex equipment, is the premise of production, which requires the condition monitoring and diagnosis of the system. Recently, the continuous development of deep learning (DL) has promoted the research of intelligent diagnosis in process industry systems, and the sensor system layout has provided sufficient data foundation for this task. However, these DL-driven approaches have had some shortcomings: (1) the output signals of heterogeneous sensing systems existing in process industry systems are often high-dimensional coupled and (2) the fault diagnosis model built from pure data lacks systematic process knowledge, resulting in inaccurate fitting. To solve these problems, a graph feature fusion-driven fault diagnosis of complex process industry systems is proposed in this paper. First, according to the system’s prior knowledge and data characteristics, the original multisource heterogeneous data are divided into two categories. On this basis, the two kinds of data are converted to physical space graphs (PSG) and process knowledge graphs (PKG), respectively, according to the physical space layout and reaction mechanism of the system. Second, the node features and system spatial features of the subgraphs are extracted by the graph convolutional neural network at the same time, and the fault representation information of the subgraph is mined. Finally, the attention mechanism is used to fuse the learned subgraph features getting the global-graph representation for fault diagnosis. Two publicly available process chemistry datasets validate the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-5c8268054cf647869fb277e2cfdf3534 |
| institution | OA Journals |
| issn | 1875-9203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-5c8268054cf647869fb277e2cfdf35342025-08-20T02:05:44ZengWileyShock and Vibration1875-92032024-01-01202410.1155/2024/9197578Graph Feature Fusion-Driven Fault Diagnosis of Complex Process Industrial System Based on Multivariate Heterogeneous DataFengyuan Zhang0Jie Liu1Xiang Lu2Tao Li3Yi Li4Yongji Sheng5Hu Wang6Yingwei Liu7School of Civil and Hydraulic EngineeringSchool of Civil and Hydraulic EngineeringHubei Key Laboratory of Material Chemistry and Service FailureHubei Key Laboratory of Material Chemistry and Service FailureCOFCO (Jilin) Bio-Chemical Technology Co., LtdCOFCO (Jilin) Bio-Chemical Technology Co., LtdCOFCO (Anhui) Bio-Chemical Technology Co., LtdCOFCO Nutrition and Health Research Institute Co., LtdThe stable operation of the process industrial system, which is integrated with various complex equipment, is the premise of production, which requires the condition monitoring and diagnosis of the system. Recently, the continuous development of deep learning (DL) has promoted the research of intelligent diagnosis in process industry systems, and the sensor system layout has provided sufficient data foundation for this task. However, these DL-driven approaches have had some shortcomings: (1) the output signals of heterogeneous sensing systems existing in process industry systems are often high-dimensional coupled and (2) the fault diagnosis model built from pure data lacks systematic process knowledge, resulting in inaccurate fitting. To solve these problems, a graph feature fusion-driven fault diagnosis of complex process industry systems is proposed in this paper. First, according to the system’s prior knowledge and data characteristics, the original multisource heterogeneous data are divided into two categories. On this basis, the two kinds of data are converted to physical space graphs (PSG) and process knowledge graphs (PKG), respectively, according to the physical space layout and reaction mechanism of the system. Second, the node features and system spatial features of the subgraphs are extracted by the graph convolutional neural network at the same time, and the fault representation information of the subgraph is mined. Finally, the attention mechanism is used to fuse the learned subgraph features getting the global-graph representation for fault diagnosis. Two publicly available process chemistry datasets validate the effectiveness of the proposed method.http://dx.doi.org/10.1155/2024/9197578 |
| spellingShingle | Fengyuan Zhang Jie Liu Xiang Lu Tao Li Yi Li Yongji Sheng Hu Wang Yingwei Liu Graph Feature Fusion-Driven Fault Diagnosis of Complex Process Industrial System Based on Multivariate Heterogeneous Data Shock and Vibration |
| title | Graph Feature Fusion-Driven Fault Diagnosis of Complex Process Industrial System Based on Multivariate Heterogeneous Data |
| title_full | Graph Feature Fusion-Driven Fault Diagnosis of Complex Process Industrial System Based on Multivariate Heterogeneous Data |
| title_fullStr | Graph Feature Fusion-Driven Fault Diagnosis of Complex Process Industrial System Based on Multivariate Heterogeneous Data |
| title_full_unstemmed | Graph Feature Fusion-Driven Fault Diagnosis of Complex Process Industrial System Based on Multivariate Heterogeneous Data |
| title_short | Graph Feature Fusion-Driven Fault Diagnosis of Complex Process Industrial System Based on Multivariate Heterogeneous Data |
| title_sort | graph feature fusion driven fault diagnosis of complex process industrial system based on multivariate heterogeneous data |
| url | http://dx.doi.org/10.1155/2024/9197578 |
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