Next Article in Journal
Application of an Eddy Current-Tuned Mass Damper to Vibration Mitigation of Offshore Wind Turbines
Previous Article in Journal
Effect of Clay Mineral Composition on Low-Salinity Water Flooding
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Energies 2018, 11(12), 3318; https://doi.org/10.3390/en11123318

Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions

1
School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100083, China
2
Department of Electrical and Computer Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada
3
Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada
*
Author to whom correspondence should be addressed.
Received: 5 November 2018 / Revised: 22 November 2018 / Accepted: 23 November 2018 / Published: 28 November 2018
(This article belongs to the Section Sustainable Energy)
Full-Text   |   PDF [4210 KB, uploaded 28 November 2018]   |  

Abstract

Wind-powered electricity generation has grown significantly over the past decade. While there are many components that might impact their useful life, the gearbox and generator bearings are among the most fragile components in wind turbines. Therefore, the prediction of remaining useful life (RUL) of faulty or damaged wind turbine bearings will provide useful support for reliability evaluation and advanced maintenance of wind turbines. This paper proposes a data-driven method combining the interval whitenization method with a Gaussian process (GP) algorithm in order to predict the RUL of wind turbine generator bearings. Firstly, a wavelet packet transform is used to eliminate noise in the vibration signals and extract the characteristic fault signals. A comprehensive analysis of the real degradation process is used to determine the indicators of degradation. The interval whitenization method is proposed to reduce the interference of non-stationary operating conditions to improve the quality of health indicators. Finally, the GP method is utilized to construct the model which reflects the relationship between the RUL and health indicators. The method is assessed using actual vibration datasets from two wind turbines. The prediction results demonstrate that the proposed method can reduce the effect of non-stationary operating conditions. In addition, compared with the support vector regression (SVR) method and artificial neural network (ANN), the prediction accuracy of the proposed method has an improvement of more than 65.8%. The prediction results verify the effectiveness and superiority of the proposed method. View Full-Text
Keywords: remaining useful life (RUL) prediction; wind turbine generator bearing; interval whitenization; Gaussian process; wavelet packet transform remaining useful life (RUL) prediction; wind turbine generator bearing; interval whitenization; Gaussian process; wavelet packet transform
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Cao, L.; Qian, Z.; Zareipour, H.; Wood, D.; Mollasalehi, E.; Tian, S.; Pei, Y. Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions. Energies 2018, 11, 3318.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top