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Traditional icing detection methods, including sensor-based and model-based approaches, heavily rely on domain knowledge, contrasting with data-centric methods. However, a balanced distribution of normal and abnormal instances in wind turbine data is imperative. In this research, we propose a framework for blade icing detection utilizing a multi-head attention mechanism-based transformer. Supervisory control and data acquisition (SCADA) data is collected from wind turbines on Hitra Island, Norway, with a 10-min average interval over 12\u00a0months. To address dimensionality challenges, an autoencoder-based data compression technique is employed, followed by the application of a multi-head attention transformer for icing detection. We investigate and compare the performance of two baseline deep learning methods: convolutional neural network (CNN) and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), against our proposed transformer framework. The results demonstrate superior accuracy and F1-score by the proposed model compared to CNN and CNN-LSTM. Additionally, we delve into a recommendation engine grounded in Bayesian inference. This engine assesses the risk associated with specific control actions, estimating conditional risk for icing and non-icing events on wind turbine blades. This Bayesian recommendation engine holds promise for real-time deployment scenarios.<\/jats:p>","DOI":"10.1007\/s00521-025-11619-2","type":"journal-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T19:15:28Z","timestamp":1757445328000},"page":"26157-26176","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi-head attention transformer and Bayesian inference recommendation engine-based blade icing detection framework for wind turbines"],"prefix":"10.1007","volume":"37","author":[{"given":"Harsh S.","family":"Dhiman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shruti","family":"Patil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shivali","family":"Wagle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nisha","family":"Soni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ketan","family":"Kotecha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7161-2109","authenticated-orcid":false,"given":"Ganeshsree","family":"Selvachandran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ajith","family":"Abraham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"key":"11619_CR1","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1016\/j.ref.2022.08.005","volume":"44","author":"K Kong","year":"2023","unstructured":"Kong K, Dyer K, Payne C, Hamerton I, Weaver PM (2023) Progress and trends in damage detection methods, maintenance, and data-driven monitoring of wind turbine blades- a review. 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