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Particularly, identifying fault types under varying working conditions holds significant importance in practical engineering. Thus, we propose a reinforcement ensemble method for diagnosing rolling bearing faults under varying working conditions. Firstly, a reinforcement model was designed to select the optimal base learner. Stratified random sampling was used to extract four datasets from raw training data. The reinforcement model was trained by these four datasets, respectively, and we obtained four optimal base learners. Then, a sparse ANN was designed as the ensemble model and the reinforcement learning model that can successfully identify the fault type under variable work conditions was constructed. Extensive experiments were conducted, and the results demonstrate the superiority of the proposed method over other intelligent approaches, with significant practical engineering benefits.<\/jats:p>","DOI":"10.3390\/s24113323","type":"journal-article","created":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T05:35:20Z","timestamp":1716442520000},"page":"3323","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Reinforcement Ensemble Learning Method for Rolling Bearing Fault Diagnosis under Variable Work Conditions"],"prefix":"10.3390","volume":"24","author":[{"given":"Yanning","family":"Li","sequence":"first","affiliation":[{"name":"School of Automation, Northwest Polytechnic University, Xi\u2019an 710072, China"},{"name":"Xi\u2019an Modern Control Technology Research Institute, Xi\u2019an 710065, China"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation, Northwest Polytechnic University, Xi\u2019an 710072, China"},{"name":"Xi\u2019an Modern Control Technology Research Institute, Xi\u2019an 710065, China"}]},{"given":"Ruixin","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Road Construction Technology & Equipment Ministry of Education, Chang\u2019an University, Xi\u2019an 710061, China"}]},{"given":"Jiangfeng","family":"Fu","sequence":"additional","affiliation":[{"name":"Advanced Power Research Institute, Northwest Polytechnic University, Xi\u2019an 710072, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101750","DOI":"10.1016\/j.aei.2022.101750","article-title":"A deep feature enhanced reinforcement learning method for rolling bearing fault diagnosis","volume":"54","author":"Wang","year":"2022","journal-title":"Adv. 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