An interpretable machine learning method for fault diagnosis of oil-immersed transformers based on edge inference
Intelligent diagnostic models using dissolved gas analysis are crucial for oil-immersed transformer fault diagnosis. However, the inherent “black box” nature of these models limits interpretability, and traditional methods that upload local data to central servers raise data security concerns. To ad...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | International Journal of Electrical Power & Energy Systems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S014206152500198X |
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| author | Dong Hu Yong Yang Hao Dai Chao Tang Jufang Xie |
| author_facet | Dong Hu Yong Yang Hao Dai Chao Tang Jufang Xie |
| author_sort | Dong Hu |
| collection | DOAJ |
| description | Intelligent diagnostic models using dissolved gas analysis are crucial for oil-immersed transformer fault diagnosis. However, the inherent “black box” nature of these models limits interpretability, and traditional methods that upload local data to central servers raise data security concerns. To address these issues, this study proposes an interpretable fault diagnosis model for edge deployment. First, a filtered feature extraction algorithm based on real domain rough set theory is proposed to optimize feature extraction before model input. Experimental results demonstrate that this algorithm enhances model performance and reduces inference time at the edge-end. Second, the hyperparameters of Extreme Gradient Boosting are automatically tuned using the Newton–Raphson optimizer. Compared with other diagnostic methods, the proposed model yields superior classification effect accuracy. Following edge-end inference, the SHapley Additive exPlanations method is employed to analyze feature impact on diagnostic results, visualizing the significance of different characteristic gases for fault types using SHAP values. Finally, the model’s robustness, reliability, and interpretability are validated through real cases, providing practical insights for transformer operation and maintenance. |
| format | Article |
| id | doaj-art-98fdf9cd4dce4968a736701871b54511 |
| institution | OA Journals |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-98fdf9cd4dce4968a736701871b545112025-08-20T01:51:54ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-07-0116811064710.1016/j.ijepes.2025.110647An interpretable machine learning method for fault diagnosis of oil-immersed transformers based on edge inferenceDong Hu0Yong Yang1Hao Dai2Chao Tang3Jufang Xie4College of Engineering and Technology, Southwest University, Chongqing, China; International R&D Center for New Technologies of Smart Grid and Equipment, Southwest University, Chongqing, China; Corresponding author at: College of Engineering and Technology, Southwest University, Chongqing, China.College of Engineering and Technology, Southwest University, Chongqing, China; International R&D Center for New Technologies of Smart Grid and Equipment, Southwest University, Chongqing, ChinaCollege of Engineering and Technology, Southwest University, Chongqing, China; International R&D Center for New Technologies of Smart Grid and Equipment, Southwest University, Chongqing, ChinaCollege of Engineering and Technology, Southwest University, Chongqing, China; International R&D Center for New Technologies of Smart Grid and Equipment, Southwest University, Chongqing, ChinaCollege of Engineering and Technology, Southwest University, Chongqing, China; International R&D Center for New Technologies of Smart Grid and Equipment, Southwest University, Chongqing, ChinaIntelligent diagnostic models using dissolved gas analysis are crucial for oil-immersed transformer fault diagnosis. However, the inherent “black box” nature of these models limits interpretability, and traditional methods that upload local data to central servers raise data security concerns. To address these issues, this study proposes an interpretable fault diagnosis model for edge deployment. First, a filtered feature extraction algorithm based on real domain rough set theory is proposed to optimize feature extraction before model input. Experimental results demonstrate that this algorithm enhances model performance and reduces inference time at the edge-end. Second, the hyperparameters of Extreme Gradient Boosting are automatically tuned using the Newton–Raphson optimizer. Compared with other diagnostic methods, the proposed model yields superior classification effect accuracy. Following edge-end inference, the SHapley Additive exPlanations method is employed to analyze feature impact on diagnostic results, visualizing the significance of different characteristic gases for fault types using SHAP values. Finally, the model’s robustness, reliability, and interpretability are validated through real cases, providing practical insights for transformer operation and maintenance.http://www.sciencedirect.com/science/article/pii/S014206152500198XDissolved gas analysisExplainable machine learningEdge-endFeature selectionGradient boostingIntelligent diagnostic models |
| spellingShingle | Dong Hu Yong Yang Hao Dai Chao Tang Jufang Xie An interpretable machine learning method for fault diagnosis of oil-immersed transformers based on edge inference International Journal of Electrical Power & Energy Systems Dissolved gas analysis Explainable machine learning Edge-end Feature selection Gradient boosting Intelligent diagnostic models |
| title | An interpretable machine learning method for fault diagnosis of oil-immersed transformers based on edge inference |
| title_full | An interpretable machine learning method for fault diagnosis of oil-immersed transformers based on edge inference |
| title_fullStr | An interpretable machine learning method for fault diagnosis of oil-immersed transformers based on edge inference |
| title_full_unstemmed | An interpretable machine learning method for fault diagnosis of oil-immersed transformers based on edge inference |
| title_short | An interpretable machine learning method for fault diagnosis of oil-immersed transformers based on edge inference |
| title_sort | interpretable machine learning method for fault diagnosis of oil immersed transformers based on edge inference |
| topic | Dissolved gas analysis Explainable machine learning Edge-end Feature selection Gradient boosting Intelligent diagnostic models |
| url | http://www.sciencedirect.com/science/article/pii/S014206152500198X |
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