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dc.contributor.authorRomero, A-
dc.contributor.authorLage, Y-
dc.contributor.authorSoua, S-
dc.contributor.authorWang, B-
dc.contributor.authorGan, TH-
dc.identifier.citationShock and Vibration, Article ID 6423587: (2016)en_US
dc.description.abstractReliable monitoring for the early fault diagnosis of gearbox faults is of great concern for the wind industry.This paper presents a novel approach for health condition monitoring (CM) and fault diagnosis in wind turbine gearboxes using vibration analysis. This methodology is based on amachine learning algorithm that generates a baseline for the identification of deviations fromthe normal operation conditions of the turbine and the intrinsic characteristic-scale decomposition (ICD) method for fault type recognition. Outliers picked up during the baseline stage are decomposed by the ICD method to obtain the product components which reveal the fault information.The new methodology proposed for gear and bearing defect identification was validated by laboratory and field trials, comparing well with the methods reviewed in the literature.en_US
dc.publisherHindawi Publishing Corporationen_US
dc.titleVestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring Systemen_US
dc.relation.isPartOfShock and Vibration-
Appears in Collections:Materials Engineering

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