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Open AccessArticle

Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data

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Departamento de Ingeniería de Explotación (INGEX), Iberdrola Ingeniería y Construcción, S.A.U., Avda. de Manoteras, 20, Edif. D, 2ª Planta, 28050 Madrid, Spain
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TENECO Research Group, Department of Mechanical Engineering, University of La Rioja, Calle San José de Calasanz, 31, 26004 Logroño, La Rioja, Spain
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Author to whom correspondence should be addressed.
Energies 2018, 11(4), 746; https://doi.org/10.3390/en11040746
Received: 19 February 2018 / Revised: 15 March 2018 / Accepted: 21 March 2018 / Published: 26 March 2018
This article offers reasons to defend the use of generic behavior models as opposed to specific models in applications to determine component degradation. The term generic models refers to models based on operating data from various units, whereas specific models are calculated using operating data taken from a single unit. Moreover, generic models, used in combination with a status indicator, show excellent capacity for detecting anomalies in the equipment and for evaluating the effectiveness of the maintenance actions, resulting in lower development and maintenance costs for the operating firm. Artificial neural networks and moving means were used to calculate the degradation indicators, based on the remainders in the model. The models were developed from operating data from fourteen wind turbines monitored over several years, and applied to the detection of faults in the bearings on the non-drive end of the generator. The use of generic models may not be recommendable for detecting faults in all cases, and the suitability will depend greatly on the context of the methodology developed to detect each type of fault, according to the element causing the fault and the fault mode, since each methodology requires a greater or lesser degree of precision in the model. View Full-Text
Keywords: condition monitoring; failure detection; wind turbines; generator bearing; SCADA data; normal behavior models condition monitoring; failure detection; wind turbines; generator bearing; SCADA data; normal behavior models
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MDPI and ACS Style

Rodríguez-López, M.A.; López-González, L.M.; López-Ochoa, L.M.; Las-Heras-Casas, J. Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data. Energies 2018, 11, 746. https://doi.org/10.3390/en11040746

AMA Style

Rodríguez-López MA, López-González LM, López-Ochoa LM, Las-Heras-Casas J. Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data. Energies. 2018; 11(4):746. https://doi.org/10.3390/en11040746

Chicago/Turabian Style

Rodríguez-López, Miguel A.; López-González, Luis M.; López-Ochoa, Luis M.; Las-Heras-Casas, Jesús. 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data" Energies 11, no. 4: 746. https://doi.org/10.3390/en11040746

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