{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:33:10Z","timestamp":1760232790385,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T00:00:00Z","timestamp":1669334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China\u2019s National Key R&amp;D Program Project","award":["2019YFB1705000"],"award-info":[{"award-number":["2019YFB1705000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The demands for model accuracy and computing efficiency in fault warning scenarios are increasing as high-speed railway train technology continues to advance. The black box model is difficult to interpret, making it impossible for this technology to be widely adopted in the railway industry, which has strict safety regulations. This paper proposes a fault early warning machine learning model based on feature contribution and causal inference. First, the contributions of the features are calculated through the Shapley additive explanations model. Then, causal relationships are discovered through causal inference models. Finally, data from causal and high-contribution time series are applied to the model. Ablation tests are conducted with the Na\u00efve Bayes, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, and other models in order to confirm the efficiency of the method based on early warning data regarding the on-site high-speed train traction equipment circuit board failure. The findings indicate that the strategy improves the evaluation markers, including the early warning accuracy, precision, recall, and F1 score, by an average of more than 10%. There is a 35% improvement in the computing efficiency, and the model can provide feature causal graph verification for expert product decision-making.<\/jats:p>","DOI":"10.3390\/s22239184","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T08:13:09Z","timestamp":1669623189000},"page":"9184","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Fault Early Warning Model for High-Speed Railway Train Based on Feature Contribution and Causal Inference"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1328-5303","authenticated-orcid":false,"given":"Dian","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Lab of Rail Traffic Control & Safety, Beijing Jiaotong University, Beijing 100044, China"},{"name":"Locomotive & Car Research Institute, China Academy of Railway Sciences Group Co., Ltd., Beijing 100081, China"}]},{"given":"Yong","family":"Qin","sequence":"additional","affiliation":[{"name":"State Key Lab of Rail Traffic Control & Safety, Beijing Jiaotong University, Beijing 100044, China"}]},{"given":"Yiying","family":"Zhao","sequence":"additional","affiliation":[{"name":"China Railway Engineering Design and Consulting Group Co., Ltd., Beijing 100055, China"}]},{"given":"Weijun","family":"Yang","sequence":"additional","affiliation":[{"name":"Locomotive & Car Research Institute, China Academy of Railway Sciences Group Co., Ltd., Beijing 100081, China"}]},{"given":"Haijun","family":"Hu","sequence":"additional","affiliation":[{"name":"Locomotive & Car Research Institute, China Academy of Railway Sciences Group Co., Ltd., Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4388-2691","authenticated-orcid":false,"given":"Ning","family":"Yang","sequence":"additional","affiliation":[{"name":"Locomotive & Car Research Institute, China Academy of Railway Sciences Group Co., Ltd., Beijing 100081, China"}]},{"given":"Bing","family":"Liu","sequence":"additional","affiliation":[{"name":"Locomotive & Car Research Institute, China Academy of Railway Sciences Group Co., Ltd., Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,25]]},"reference":[{"key":"ref_1","first-page":"80","article-title":"Development and Practice of PHM Oriented High-Speed Train Pedigree Product Technology Platform","volume":"42","author":"Jun","year":"2021","journal-title":"China Railw. 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