{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T04:26:08Z","timestamp":1778559968258,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Wheel-Rail Interaction Laboratory at the National Engineering Research Center for High-Speed Rail and Urban Rail Transit Systems","award":["2024YJ131"],"award-info":[{"award-number":["2024YJ131"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Owing to the intricate mechanical coupling characteristics and the considerable difficulty in extracting synergistic spatio-temporal features from high-dimensional sensor data under fluctuating alternating loads, this study proposes a robust anomaly detection framework that combines Normalized Mutual Information (NMI) and Spatio-Temporal Graph Neural Networks (STGNN). First, NMI is utilized to quantify the nonlinear physical coupling intensity among multi-source sensors, thereby filtering out weakly correlated noise and reconstructing the spatial topological structure of the coupler system. Subsequently, a deep learning architecture incorporating Graph Convolutional Networks (GCN), Gated Recurrent Units (GRU), and one-dimensional convolutional residual connections is developed to capture the dynamic evolutionary characteristics of equipment states across both spatial interactions and temporal sequences. Finally, based on the model\u2019s health-state predictions, a moving average algorithm is introduced to smooth the residual sequences, and an anomaly early-warning baseline is established in conjunction with the 3\u03c3 criterion. Experimental validation conducted using field service data from heavy-haul trains demonstrates that, compared to conventional serial CNN and Long Short-Term Memory (LSTM) models, the proposed method exhibits superior fitting performance and robustness against noise, effectively reducing the false alarm rate within normal working intervals. In a real-world case study, the method successfully identified variations in spatial linkage features induced by local damage and triggered timely alerts. Notably, the spatial alarm nodes were highly consistent with the fatigue crack initiation sites identified through on-site magnetic particle inspection. This study provides a viable data-driven analytical framework for the condition monitoring and anomaly identification of critical load-bearing components in heavy-haul trains.<\/jats:p>","DOI":"10.3390\/s26092661","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T11:40:12Z","timestamp":1777981212000},"page":"2661","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Spatio-Temporal Joint Network for Coupler Anomaly Detection Under Complex Working Conditions Utilizing Multi-Source Sensors"],"prefix":"10.3390","volume":"26","author":[{"given":"Zhirong","family":"Zhao","sequence":"first","affiliation":[{"name":"Rolling Stock Branch, CHN Energy Shuohuang Railway Development Co., Ltd., Cangzhou 062350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhentian","family":"Jiang","sequence":"additional","affiliation":[{"name":"Rolling Stock Branch, CHN Energy Shuohuang Railway Development Co., Ltd., Cangzhou 062350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2976-1412","authenticated-orcid":false,"given":"Long","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3576-7254","authenticated-orcid":false,"given":"Jinbo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.1080\/00423114.2024.2374446","article-title":"Research on Abnormal Wear Mechanism of the Coupler System in Heavy-Haul Trains","volume":"63","author":"Hu","year":"2024","journal-title":"Veh. 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