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Predicting the remaining useful life (RUL) of these bearings is challenging due to their complex working conditions. This study presents an RUL prediction methodology that combines deep learning networks and autoregression (AR). The deep learning network capitalizes on its high prediction accuracy for short\u2010term prediction, while AR leverages its robustness and resistance to interference to depict the long\u2010term declining trend in RUL. The fusion of these two approaches enables precise RUL prediction. The network model introduced in this paper integrates a temporal convolutional network (TCN) with a lightweight channel attention mechanism (LCAM) and runs them in parallel with a nonlinear neural network AR module. The outputs from both models are summed up to generate final prediction results, establishing an end\u2010to\u2010end RUL prediction framework. The efficacy of this methodology is confirmed through its application to the laboratory mechanical components accelerated aging fatigue test rig dataset. A comparative analysis reveals that the proposed AR\u2010LCAM\u2010TCN framework surpasses other state\u2010of\u2010the\u2010art methods in terms of RUL and system prediction accuracy, as well as computational efficiency.<\/jats:p>","DOI":"10.1002\/qre.70147","type":"journal-article","created":{"date-parts":[[2025,12,28]],"date-time":"2025-12-28T10:04:31Z","timestamp":1766916271000},"page":"1321-1332","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Remaining Useful Life Prediction for ATS Bearings Based on a Temporal Convolutional Network Fused With Autoregression"],"prefix":"10.1002","volume":"42","author":[{"given":"Runxia","family":"Guo","sequence":"first","affiliation":[{"name":"College of Electronic Information and Automation Civil Aviation University of China  Tianjin China"}]},{"given":"Xianfeng","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Electronic Information and Automation Civil Aviation University of China  Tianjin China"}]},{"given":"Siying","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic Information and Automation Civil Aviation University of China  Tianjin China"}]},{"given":"Chao","family":"Huang","sequence":"additional","affiliation":[{"name":"Sino\u2010European Institute of Aviation Engineering Civil Aviation University of China  Tianjin China"}]}],"member":"311","published-online":{"date-parts":[[2025,12,28]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.18494\/SAM4111"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engfailanal.2021.105616"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/act11030067"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106501\/acbed0"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/machines9100210"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40430\u2010022\u201003968\u2010z"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.108330"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.seta.2021.101416"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10479\u2010022\u201004575\u2010w"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS\u2010212873"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21030932"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMECH.2021.3094986"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s44176\u2010023\u201000019\u20102"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3291371"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.02.045"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2024.3402660"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106501\/ad5223"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111493"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/1830694"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.17576\/jsm\u20102022\u20105111\u201022"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40430\u2010022\u201003856\u20106"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2024.115012"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2022.10.009"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.107868"},{"key":"e_1_2_8_26_1","unstructured":"S.Bai J. 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