A review of predictive techniques used to support decision making for maintenance operations of wind turbines

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dc.contributor.authorPandit, Ravi
dc.contributor.authorAstolfi, Davide
dc.contributor.authorDurazo-Cardenas, Isidro
dc.date.accessioned2023-03-22T16:24:06Z
dc.date.available2023-03-22T16:24:06Z
dc.date.issued2023-02-07
dc.description.abstractThe analysis of reliable studies helps to identify the credibility, scope, and limitations of various techniques for condition monitoring of a wind turbine (WT) system’s design and development to reduce the operation and maintenance (O&M) costs of the WT. In this study, recent advancements in data-driven models for condition monitoring and predictive maintenance of wind turbines’ critical components (e.g., bearing, gearbox, generator, blade pitch) are reviewed. We categorize these models according to data-driven procedures, such as data descriptions, data pre-processing, feature extraction and selection, model selection (classification, regression), validation, and decision making. Our findings after reviewing extensive relevant articles suggest that (a) SCADA (supervisory control and data acquisition) data are widely used as they are available at low cost and are extremely practical (due to the 10 min averaging time), but their use is in some sense nonspecific. (b) Unstructured data and pre-processing remain a significant challenge and consume a significant time of whole machine learning model development. (c) The trade-off between the complexity of the vibration analysis and the applicability of the results deserves further development, especially with regards to drivetrain faults. (d) Most of the proposed techniques focus on gearbox and bearings, and there is a need to apply these models to other wind turbine components. We explain these findings in detail and conclude with a discussion of the main areas for future work in this domain.en_UK
dc.identifier.citationPandit RK, Astolfi D, Durazo Cardenas I. (2023) A review of predictive techniques used to support decision making for maintenance operations of wind turbines. Energies, Volume 16, Issue 4, February 2023, Article number 1654en_UK
dc.identifier.issn1996-1073
dc.identifier.urihttps://doi.org/10.3390/en16041654
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19341
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectwind turbineen_UK
dc.subjectpredictive maintenanceen_UK
dc.subjectbig data computationen_UK
dc.subjectcondition monitoringen_UK
dc.subjectdata-driven modelsen_UK
dc.titleA review of predictive techniques used to support decision making for maintenance operations of wind turbinesen_UK
dc.typeArticleen_UK

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