{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:04:56Z","timestamp":1777421096396,"version":"3.51.4"},"reference-count":56,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,7]],"date-time":"2020-11-07T00:00:00Z","timestamp":1604707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010193","name":"Korea Electric Power Corporation","doi-asserted-by":"publisher","award":["R17GA08"],"award-info":[{"award-number":["R17GA08"]}],"id":[{"id":"10.13039\/501100010193","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Boiler waterwall tube leakage is the most probable cause of failure in steam power plants (SPPs). The development of an intelligent tube leak detection system can increase the efficiency and reliability of modern power plants. The idea of e-maintenance based on multivariate algorithms was recently introduced for intelligent fault detection and diagnosis in SPPs. However, these multivariate algorithms are highly dependent on the number of input process variables (sensors). Therefore, this work proposes a machine learning-based model integrated with an optimal sensor selection scheme to analyze boiler waterwall tube leakage. Finally, a real SPP test case is employed to validate the proposed model\u2019s effectiveness. The results indicate that the proposed model can successfully detect waterwall tube leakage with improved accuracy vs. other comparable models.<\/jats:p>","DOI":"10.3390\/s20216356","type":"journal-article","created":{"date-parts":[[2020,11,8]],"date-time":"2020-11-08T19:03:37Z","timestamp":1604862217000},"page":"6356","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Intelligent Steam Power Plant Boiler Waterwall Tube Leakage Detection via Machine Learning-Based Optimal Sensor Selection"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2037-0928","authenticated-orcid":false,"given":"Salman","family":"Khalid","sequence":"first","affiliation":[{"name":"Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, Seoul 04620, Korea"}]},{"given":"Woocheol","family":"Lim","sequence":"additional","affiliation":[{"name":"Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, Seoul 04620, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7057-5174","authenticated-orcid":false,"given":"Heung Soo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, Seoul 04620, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3642-1830","authenticated-orcid":false,"given":"Yeong Tak","family":"Oh","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Seoul National University, Seoul 08826, Korea"}]},{"given":"Byeng D.","family":"Youn","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Seoul National University, Seoul 08826, Korea"}]},{"given":"Hee-Soo","family":"Kim","sequence":"additional","affiliation":[{"name":"Korea Electric Power Research Institute, Daejeon 34056, Korea"}]},{"given":"Yong-Chae","family":"Bae","sequence":"additional","affiliation":[{"name":"Korea Electric Power Research Institute, Daejeon 34056, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1016\/j.rser.2010.12.007","article-title":"Energy and exergy analyses of thermal power plants: A review","volume":"15","author":"Kaushik","year":"2011","journal-title":"Renew. 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