A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines

Ravi Kumar Pandit (), Davide Astolfi and Isidro Durazo Cardenas
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Ravi Kumar Pandit: Centre for Life-Cycle Engineering and Management, Cranfield University, Bedford MK43 0AL, UK
Davide Astolfi: Department of Engineering, University of Perugia, Via G. Duranti, 06125 Perugia, Italy
Isidro Durazo Cardenas: Centre for Life-Cycle Engineering and Management, Cranfield University, Bedford MK43 0AL, UK

Energies, 2023, vol. 16, issue 4, 1-17

Abstract: The 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.

Keywords: wind turbine; predictive maintenance; big data computation; condition monitoring; data-driven models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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