{"id":13176,"date":"2026-10-17T05:21:42","date_gmt":"2026-10-16T21:21:42","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=13176"},"modified":"2026-10-17T06:21:28","modified_gmt":"2026-10-16T22:21:28","slug":"jase-202701-36-030","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202701-36-030","title":{"rendered":"Artificial Intelligence-Based Degradation Assessment and Remaining Useful Life Prediction for 10 kV Tulip Contacts via Thermal\u2013Electrical\u2013Mechanical Feature Fusion"},"content":{"rendered":"\n
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<\/i> 2027<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 36<\/a><\/p>\n<\/div>\n\n\n\n

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Bo Yuan1<\/sup>, Xielin Shen1<\/sup>, Hua Ye1<\/sup><\/i><\/a>, Lei Wang2,3<\/sup>, and Yiming Liu2,3<\/sup><\/p>\n\n\n\n

1<\/sup>Quanzhou Power Supply Company, State Grid Fujian Electric Power Co., Ltd., Quanzhou, 362064, China<\/p>\n\n\n\n

2<\/sup>College of Automation (College of Artificial Intelligence), Beijing Information Science & Technology University, Beijing, 100192, China<\/p>\n\n\n\n

3<\/sup>Beijing Key Laboratory of Low-Carbon and Flexible Urban Power Supply Technology and Equipment, Beijing, 100192, China<\/p>\n<\/div>\n\n\n\n

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Received: August 07, 2026
Accepted: September 07, 2026
Publication Date: October 17, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Overall architecture of the proposed PI-TEMFNet<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202701_36.030<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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To address the coupled degradation of tulip contacts in 10 kV withdrawable switchgear under the combined effects of insertion-withdrawal wear, spring relaxation, increasing contact resistance, and localized overheating, this paper proposes a physics-informed thermal-electrical-mechanical feature fusion network (PI-TEMFNet). The method extracts multi-rate features, including temperature rise, contact resistance, contact force, and vibration, and employs reliability-aware masked cross-modal attention to fuse asynchronous and missing information. Contact-resistance relationships, the heat-balance equation, and a monotonic degradation constraint are embedded into the joint prediction of degradation stage, health index, and probabilistic remaining useful life (RUL). Accelerated degradation tests on 12 GC5 specimens and leave-one-specimen-out cross-validation show that the model achieves an Accuracy of 96.4%, a Macro-F1 of 96.1%, and an AUC of 0.988, with a health-index RMSE of 0.041. The MAE, RMSE, and MAPE for RUL prediction are 21.6\u00d710\u00b3 cycles, 29.4\u00d7103<\/sup> cycles, and 5.8%, respectively, while the coverage probability of the 90% prediction interval is 91.3%. Under random missing rates of 20% and 40%, the MAPE remains 6.3% and 7.2%, respectively, demonstrating high accuracy and strong robustness.<\/p>\n\n\n\n

Keywords: 10 kV switchgear; tulip contact; thermal-electrical-mechanical feature fusion; physics-informed neural network; degradation assessment; health index; remaining useful life; probabilistic prediction<\/em><\/p>\n\n\n\n

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