[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121471-en":3,"doc-seo-121471-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121471,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Leveraging signal processing and machine learning for automated fault detection in wind turbine drivetrains - Hybrid method for drivetrain health overview","Wind energy is a sustainable renewable source but faces high operating and maintenance costs, especially offshore. The research presents a hybrid fault detection approach that merges physical signal-processing knowledge with machine learning to assess the health of wind turbine drivetrain components. Vibration signals from accelerometers are transformed into many condition indicators, while models trained on SCADA and healthy-operation data label indicators as healthy or faulty over time. Fused indicator labels yield an operator-friendly health overview, validated on multi-year offshore monitoring data.","Wind Energ. Sci., 10, 1963–1978, 2025  \n[https://doi.org/10.5194/wes-10-1963-2025](https://doi.org/10.5194/wes-10-1963-2025)[ ](https://doi.org/10.5194/wes-10-1963-2025)© Author(s) 2025 . This work is distributed under the Creative Commons Attribution 4 .0 License.  \nLeveraging signal processing and machine learning for automated fault detection in wind turbine drivetrains  \nFaras Jamil 1,2 , Cédric Peeters 1 , Timothy Verstraeten 1,2 , and Jan Helsen 1  \n1Acoustics & Vibration Research Group/OWI-Lab, Vrije Universiteit Brussel,  \nPleinlaan 2, 1050 Elsene, Belgium  \n2Artificial Intelligence Lab Brussels, Vrije Universiteit Brussel,  \nPleinlaan 9, 1050 Elsene, Belgium  \nCorrespondence: Faras Jamil ([faras.jamil@vub.be](faras.jamil@vub.be))  \nReceived: 12 September 2024 – Discussion started: 25 November 2024  \nRevised: 16 May 2025 – Accepted: 23 June 2025 – Published: 11 September 2025  \nAbstract. Wind energy is considered a sustainable renewable energy source; however, it faces the challenge of significant operating and maintenance costs. The research proposes a hybrid fault detection method to combine the physical domain knowledge with the machine learning models to provide an overview of the health of wind turbine drivetrain components. Signal processing indicators are computed from raw vibration signals measured from strategically placed accelerometers over drivetrain components. It produces an immense number of indicators as each indicator is sensitive towards certain types of faults, and manual monitoring becomes an unfeasible task. The machine learning models are trained using signal processing indicators and supervisory control and data acquisition (SCADA) data. The normal behavior modeling technique is employed to learn the healthy operation of the machine from data collected during healthy machine operation. The trained normal behavior machine learning models label each indicator in a healthy or faulty state over time. The labeled state-of-the-art signal processing indicators are fused to provide a high-level health status overview of wind turbine drivetrain components. It helps to derive the required details from many condition indicators, which is valuable when managing multiple components in a single wind turbine across an entire wind farm. The proposed hybrid fault detection method is validated on an offshore wind farm with multiple years of condition monitoring data. It provides a high-level health overview that is readily understandable for non-expert wind farm operators, and for more detailed fault analysis, experts can conduct a comprehensive inspection.  \n1 Introduction  \nRenewable energy has experienced significant growth in recent years and has reduced the impact of global warming. In 2022, international investments in the renewable energy sector reached USD 1 .3 trillion to decarbonize fossil-fuelbased energy production (IRENA and CPI, 2023) . The growing interest in renewable energy has led to a substantial increase in clean, green energy production. The global installed wind energy capacity has escalated to 906 GW due to the fast growth observed during recent years (Hutchinson and Zhao, 2023). The increasing interest in renewable and wind energy is accompanied by the challenge of significant operating and maintenance (O&M) costs. In the case of offshore wind, the  \nO&M cost accounts for 30 % of the total energy cost, primarily due to the remote and challenging environmental conditions of offshore locations. Offshore wind energy sites are advantageous for wind energy due to the availability of more consistent and strong winds to harvest (Gao and Odgaard, 2023) . This situation offers ample opportunities for cost reduction in offshore wind energy by identifying faults at early stages to plan efficient group maintenance strategies by combining multiple wind turbines or components (Wang et al., 2022b) . It is crucial to accurately determine the health status of wind turbines to plan efficient maintenance strategies","cbCaikL1bNuxq9Y9","https://ap.wps.com/l/cbCaikL1bNuxq9Y9","pdf",3916606,1,16,"English","en",105,"# Introduction\n## Motivation and O&M cost challenge\n## Industry 4.0, IoT sensing, and predictive maintenance\n# Proposed hybrid fault detection approach\n## Indicator generation from vibration signals\n## Machine learning normal behavior modeling\n## Fusion into high-level health status\n# Validation and results\n## Offshore wind farm case study","[{\"question\":\"What problem does the proposed method address for wind turbine maintenance?\",\"answer\":\"It targets the high operating and maintenance costs by enabling earlier and more accurate identification of drivetrain faults, supporting predictive maintenance planning.\"},{\"question\":\"How are fault-related signals converted into inputs for machine learning?\",\"answer\":\"Raw vibration signals measured by strategically placed accelerometers are processed to compute signal-processing indicators that respond to different fault types.\"},{\"question\":\"How does the method produce a health status that non-experts can use?\",\"answer\":\"Machine learning models label each indicator as healthy or faulty over time, and the labeled indicators are fused to generate a high-level drivetrain health overview.\"}]","Leveraging signal processing and machine learning for automated fault detection in wind turbine drivetrains - 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