{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T16:17:37Z","timestamp":1785428257226,"version":"3.56.0"},"reference-count":50,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&D Program","award":["2022YFF0608700"],"award-info":[{"award-number":["2022YFF0608700"]}]},{"name":"Open Fund of the National Key Laboratory for Offshore Wind Power Equipment and High-Efficiency Utilization of Wind Energy","award":["HFQZS2025-06"],"award-info":[{"award-number":["HFQZS2025-06"]}]},{"name":"Shandong Xiehe University","award":["SDXHQD2025044"],"award-info":[{"award-number":["SDXHQD2025044"]}]},{"award":["SDXHQD2025044"],"award-info":[{"award-number":["SDXHQD2025044"]}],"id":[{"id":"https:\/\/ror.org\/01bx4e159","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>To effectively extract fault characteristics from complex vibration signals and improve the diagnostic performance of deep learning networks, this paper introduces a wind turbine blade fault diagnosis method that combines Multi-scale Enhanced Hierarchical Fuzzy Entropy (MEHFE), Isolation Forest, and the Grey Wolf Optimization (GWO) algorithm for optimizing the Gated Recurrent Unit (GRU). Initially, the MEHFE algorithm is applied to decompose and reconstruct three-directional vibration signals at the blade root, thereby extracting \u201cscale-frequency\u201d dual-dimensional features that represent the evolution of fault frequency structure and complexity across multiple scales. Subsequently, Isolation Forest is employed to assess and filter feature importance, constructing an optimal feature subset to mitigate redundancy and noise interference. Finally, the optimal features are fed into the GRU network for fault pattern recognition, and the GWO algorithm is utilized to adaptively optimize network hyperparameters, thereby enhancing classification accuracy and noise resilience. Simulation experiments on typical wind turbine blade faults reveal that when GRU serves as the classifier, the diagnostic accuracy of MEHFE exceeds 76%. After feature optimization with Isolation Forest and network parameter optimization with GWO, the diagnostic accuracy surpasses 93%, demonstrating notable advantages in both classification capability and stability. Even under conditions of noise interference, the accuracy remains above 90%. The research substantiates that the proposed method can effectively extract pattern information indicative of blade structural damage from vibration data, achieving high fault recognition accuracy and robustness.<\/jats:p>","DOI":"10.3390\/e28070810","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T03:12:38Z","timestamp":1784257958000},"page":"810","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Wind Turbine Blade Fault Diagnosis Integrating Multi-Scale Enhanced Hierarchical Fuzzy Entropy, Isolation Forest and GWO-GRU"],"prefix":"10.3390","volume":"28","author":[{"given":"Min","family":"Wang","sequence":"first","affiliation":[{"name":"School of Engineering Science, Shandong Xiehe University, Jinan 250107, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8256-0401","authenticated-orcid":false,"given":"Xiao-Fei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Hunan University, Changsha 410072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guo-Jun","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Engineering Science, Shandong Xiehe University, Jinan 250107, China"},{"name":"College of Electrical and Information Engineering, Hunan University, Changsha 410072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Liu","sequence":"additional","affiliation":[{"name":"SinoAzure Wind Power Co., Ltd., Xiangtan 411101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"key":"ref_1","first-page":"175","article-title":"Review of fault diagnosis and health monitoring for wind power equipment","volume":"31","author":"Chen","year":"2020","journal-title":"China Mech. 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