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International Journal of Sustainable Energy Volume 40, 2021 - Issue 10

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Condition monitoring systems: a systematic literature review on machine-learning methods improving offshore-wind turbine operational management

Innes Murdo BlackNaval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow, UKView further author information

Mark RichmondNaval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow, UKView further author information

Athanasios KoliosNaval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow, UKCorrespondence[email protected]

https://orcid.org/0000-0001-6711-641X View further author information

Pages 923-946 | Received 15 Dec 2020, Accepted 05 Feb 2021, Published online: 11 Mar 2021

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https://doi.org/10.1080/14786451.2021.1890736
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Prince Waqas Khan, Chan Yeob Yeun & Yung Cheol Byun. (2023) Fault detection of wind turbines using SCADA data and genetic algorithm-based ensemble learning. Engineering Failure Analysis 148, pages 107209.
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Jiayang Liu, Xiaosun Wang, Fuqi Xie, Shijing Wu & Deng Li. (2023) Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network. Engineering Applications of Artificial Intelligence 121, pages 106000.
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Ravi Pandit, Davide Astolfi, Jiarong Hong, David Infield & Matilde Santos. (2022) SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends. Wind Engineering 47:2, pages 422-441.
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David Pérez Granados, Mauricio Alberto Ortega Ruiz, Joel Moreira Acosta, Sergio Arturo Gama Lara, Roberto Adrián González Domínguez & Pedro Jacinto Páramo Kañetas. (2023) A Wind Turbine Vibration Monitoring System for Predictive Maintenance Based on Machine Learning Methods Developed under Safely Controlled Laboratory Conditions. Energies 16:5, pages 2290.
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Baher Azzam, Ralf Schelenz, Martin Cardaun & Georg Jacobs. (2022) From Simulations to Accelerated Testing: Design of Experiments for Accelerated Load Testing of a Wind Turbine Drivetrain Based on Aeroelastic Multibody Simulation Data. Applied Sciences 13:1, pages 356.
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Jingwei Huang. (2022) Digital engineering transformation with trustworthy AI towards industry 4.0: emerging paradigm shifts. Journal of Integrated Design and Process Science, pages 1-22.
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Yuanchi Ma, Yongqian Liu, Zhiling Yang, Jie Yan, Tao Tao & David Infield. (2022) Research on the Derated Power Data Identification Method of a Wind Turbine Based on a Multi-Gaussian–Discrete Joint Probability Model. Sensors 22:22, pages 8891.
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Xiangqing Yin, Yi Liu, Li Yang & Wenchao Gao. (2022) Abnormal Data Cleaning Method for Wind Turbines Based on Constrained Curve Fitting. Energies 15:17, pages 6373.
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Innes Murdo Black, Debora Cevasco & Athanasios Kolios. (2022) Deep Neural Network Hard Parameter Multi-Task Learning for Condition Monitoring of an Offshore Wind Turbine. Journal of Physics: Conference Series 2265:3, pages 032091.
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X Chesterman, T Verstraeten, PJ Daems, F Perez Sanjines, A Nowé & J Helsen. (2022) The detection of generator bearing failures on wind turbines using machine learning based anomaly detection. Journal of Physics: Conference Series 2265:3, pages 032066.
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Harriet Fox, Ajit C. Pillai, Daniel Friedrich, Maurizio Collu, Tariq Dawood & Lars Johanning. (2022) A Review of Predictive and Prescriptive Offshore Wind Farm Operation and Maintenance. Energies 15:2, pages 504.
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Matteo Cardoni, Danilo Pietro Pau, Laura Falaschetti, Claudio Turchetti & Marco Lattuada. (2021) Online Learning of Oil Leak Anomalies in Wind Turbines with Block-Based Binary Reservoir. Electronics 10:22, pages 2836.
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Condition monitoring systems: a systematic literature review on machine-learning methods improving offshore-wind turbine operational management

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Condition monitoring systems: a systematic literature review on machine-learning methods improving offshore-wind turbine operational management

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