An EPS-kNN Fault Propagation Analysis Method for Industrial Processes Based on the Fusion of Knowledge and Data
To reduce the false connections resulting from numerous redundant paths in the fault propagation analysis for industrial processes, an equal probability symbolized k-nearest neighbors (EPS-kNN) fault propagation analysis method based on the fusion of fault data and process knowledge is proposed. Sub...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10955191/ |
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| _version_ | 1849311876258725888 |
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| author | Wang Lixin Qian Xiaoyi Feng Yanliang Dai Ziheng Kang Changsheng Guan Shuai Zhao Yi |
| author_facet | Wang Lixin Qian Xiaoyi Feng Yanliang Dai Ziheng Kang Changsheng Guan Shuai Zhao Yi |
| author_sort | Wang Lixin |
| collection | DOAJ |
| description | To reduce the false connections resulting from numerous redundant paths in the fault propagation analysis for industrial processes, an equal probability symbolized k-nearest neighbors (EPS-kNN) fault propagation analysis method based on the fusion of fault data and process knowledge is proposed. Subblock interaction monitoring is introduced to identify potential fault areas and eliminate redundant variables. The complex network model is incorporated into the EPS-kNN-based fault propagation path identification to improve accuracy and interpretability. Different types of faults in the Tennessee Eastman process and Ammonia synthesis process are applied to verify the effectiveness, and the result shows that the proposed method can identify the fault propagation path more effectively and reduce the occurrence of false connections compared with the traditional methods. |
| format | Article |
| id | doaj-art-4ad2a761bcde44fbb04681cc6e3efec6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4ad2a761bcde44fbb04681cc6e3efec62025-08-20T03:53:17ZengIEEEIEEE Access2169-35362025-01-0113783357834710.1109/ACCESS.2025.355863110955191An EPS-kNN Fault Propagation Analysis Method for Industrial Processes Based on the Fusion of Knowledge and DataWang Lixin0https://orcid.org/0009-0009-9281-0249Qian Xiaoyi1https://orcid.org/0000-0002-3926-6813Feng Yanliang2Dai Ziheng3https://orcid.org/0009-0002-5596-5734Kang Changsheng4https://orcid.org/0009-0006-3994-7834Guan Shuai5Zhao Yi6Liaoning Key Laboratory of Power Grid Energy Conservation and Control, Shenyang Institute of Engineering, Shenyang, ChinaLiaoning Key Laboratory of Power Grid Energy Conservation and Control, Shenyang Institute of Engineering, Shenyang, ChinaState Grid Liaoning Electric Power Company Ltd., Yingkou Power Supply Company, Yingkou, ChinaLiaoning Key Laboratory of Power Grid Energy Conservation and Control, Shenyang Institute of Engineering, Shenyang, ChinaLiaoning Key Laboratory of Power Grid Energy Conservation and Control, Shenyang Institute of Engineering, Shenyang, ChinaLiaoning Key Laboratory of Power Grid Energy Conservation and Control, Shenyang Institute of Engineering, Shenyang, ChinaLiaoning Key Laboratory of Power Grid Energy Conservation and Control, Shenyang Institute of Engineering, Shenyang, ChinaTo reduce the false connections resulting from numerous redundant paths in the fault propagation analysis for industrial processes, an equal probability symbolized k-nearest neighbors (EPS-kNN) fault propagation analysis method based on the fusion of fault data and process knowledge is proposed. Subblock interaction monitoring is introduced to identify potential fault areas and eliminate redundant variables. The complex network model is incorporated into the EPS-kNN-based fault propagation path identification to improve accuracy and interpretability. Different types of faults in the Tennessee Eastman process and Ammonia synthesis process are applied to verify the effectiveness, and the result shows that the proposed method can identify the fault propagation path more effectively and reduce the occurrence of false connections compared with the traditional methods.https://ieeexplore.ieee.org/document/10955191/Complex network modelfault propagation pathk-nearest neighborssubblock interaction monitoring |
| spellingShingle | Wang Lixin Qian Xiaoyi Feng Yanliang Dai Ziheng Kang Changsheng Guan Shuai Zhao Yi An EPS-kNN Fault Propagation Analysis Method for Industrial Processes Based on the Fusion of Knowledge and Data IEEE Access Complex network model fault propagation path k-nearest neighbors subblock interaction monitoring |
| title | An EPS-kNN Fault Propagation Analysis Method for Industrial Processes Based on the Fusion of Knowledge and Data |
| title_full | An EPS-kNN Fault Propagation Analysis Method for Industrial Processes Based on the Fusion of Knowledge and Data |
| title_fullStr | An EPS-kNN Fault Propagation Analysis Method for Industrial Processes Based on the Fusion of Knowledge and Data |
| title_full_unstemmed | An EPS-kNN Fault Propagation Analysis Method for Industrial Processes Based on the Fusion of Knowledge and Data |
| title_short | An EPS-kNN Fault Propagation Analysis Method for Industrial Processes Based on the Fusion of Knowledge and Data |
| title_sort | eps knn fault propagation analysis method for industrial processes based on the fusion of knowledge and data |
| topic | Complex network model fault propagation path k-nearest neighbors subblock interaction monitoring |
| url | https://ieeexplore.ieee.org/document/10955191/ |
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