{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T19:23:01Z","timestamp":1775848981006,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["52276003"],"award-info":[{"award-number":["52276003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Grant of Guoneng Nanjing Electric Power Test & Research Limited","award":["DY2024Y02"],"award-info":[{"award-number":["DY2024Y02"]}]},{"name":"Science and Technology Innovation Project of China Energy Investment Corporation","award":["GJNY-23-68"],"award-info":[{"award-number":["GJNY-23-68"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["2242025K30015"],"award-info":[{"award-number":["2242025K30015"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Currently, thermal power units undertake the task of peak and frequency regulation, and their internal equipment is in a non-conventional environment, which could very easily fail and thus lead to unplanned shutdown of the unit. To realize the condition monitoring and early warning of the key equipment inside coal power units, this study proposes a deep learning-based equipment condition anomaly detection model, which combines the deep autoencoder (DAE), Transformer, and Gaussian mixture model (GMM) to establish an anomaly detection model. DAE and the Transformer encoder extract static and time-series features from multi-dimensional operation data, and GMM learns the feature distribution of normal data to realize anomaly detection. Based on the data verification of boiler superheater equipment and turbine bearings in real power plants, the model is more capable of detecting equipment anomalies in advance than the traditional method and is more stable with fewer false alarms. When applied to the superheater equipment, the proposed model triggered early warnings approximately 90 h in advance compared to the actual failure time, with a lower false negative rate, reducing the missed detection rate by 70% compared to the Transformer-GMM (TGMM) model, which verifies the validity of the model and its early warning capability.<\/jats:p>","DOI":"10.3390\/a18110704","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T17:06:06Z","timestamp":1762362366000},"page":"704","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Hybrid Deep Learning Framework for Anomaly Detection in Power Plant Systems"],"prefix":"10.3390","volume":"18","author":[{"given":"Shuchong","family":"Wang","sequence":"first","affiliation":[{"name":"Guoneng Nanjing Electric Power Test & Research Limited, Nanjing 210023, China"}]},{"given":"Changxiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Guoneng Nanjing Electric Power Test & Research Limited, Nanjing 210023, China"}]},{"given":"Xingchen","family":"Liu","sequence":"additional","affiliation":[{"name":"Guoneng Nanjing Electric Power Test & Research Limited, Nanjing 210023, China"}]},{"given":"Xianghong","family":"Ni","sequence":"additional","affiliation":[{"name":"Guoneng Nanjing Electric Power Test & Research Limited, Nanjing 210023, China"}]},{"given":"Xu","family":"Chen","sequence":"additional","affiliation":[{"name":"Guoneng Nanjing Electric Power Test & Research Limited, Nanjing 210023, China"}]},{"given":"Xinglong","family":"Gao","sequence":"additional","affiliation":[{"name":"China Energy Changzhou Secondary Power Generation CO., Ltd., Changzhou 213001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8960-8773","authenticated-orcid":false,"given":"Li","family":"Sun","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Power Generation Control and Safety, School of Energy and Environment, Southeast University, Nanjing 210096, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fausing Olesen, J., and Shaker, H.R. 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