Wind turbine gearbox failure and remaining useful life prediction using machine learning techniques. Issue 3 (6th November 2018)

• Record Type: Journal Article
• Authors: Carroll, James; Koukoura, Sofia; McDonald, Alasdair; Charalambous, Anastasis; Weiss, Stephan; McArthur, Stephen
• Is Part Of: Wind energy. Volume 22:Issue 3(2019)
• Page Start: 360
• Page End: 375
• Publication Date: 2018-11-06
• Subjects: failure -- gearbox -- machine learning -- prediction -- remaining useful life -- SCADA -- vibration -- wind turbine; Wind power -- Periodicals; 621.312136
• DOI: 10.1002/we.2290
• Languages: English
• ISSNs: 1095-4244

Abstract

Abstract: This research investigates the prediction of failure and remaining useful life (RUL) of gearboxes for modern multi‐megawatt wind turbines. Failure and RUL are predicted through the use of machine learning techniques and large amounts of labelled wind turbine supervisory control and data acquisition (SCADA) and vibration data. The novelty of this work stems from unprecedented access to one of the world's largest wind turbine operational and reliability databases, containing thousands of turbine gearbox failure examples and complete SCADA and vibration data in the build up to those failures. Through access to that data, this paper is unique in having enough failure examples and data to draw the conclusions detailed in the remainder of this abstract. This paper shows that artificial neural networks provide the most accurate failure and RUL prediction out of three machine learning techniques trialled. This work also demonstrates that SCADA data can be used to predict failure up to a month before it occurs, and high frequency vibration data can be used to extend that accurate prediction capability to 5 to 6 months before failure. This paper demonstrates that two class neural networks can correctly predict gearbox failures between 72.5% and 75% of the time depending on the failure mode when trained with SCADA data and 100% of the time when trained with vibration data. Data trends in the build up to failure and weighting of the SCADA data inputs are also provided. Lastly, this work shows how multi‐class neural networks demonstrate more potential in predicting gearbox failure when trained with vibration data as opposed to training with SCADA data.