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Early Fault Diagnostic System for Rolling Bearing Faults in Wind Turbines

Xu, Libowen and Wang, Qing and Ivrissimtzis, Ioannis and Li, Shisong (2022) 'Early Fault Diagnostic System for Rolling Bearing Faults in Wind Turbines.', Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, 5 (1). 011004.


The operation and maintenance costs of wind farms are always high due to high labor costs and the high replacement cost of parts. Thus, it is of great importance to have real-time monitoring and an early fault diagnostic system to prevent major events, reduce time-based maintenance, and minimize the cost. In this paper, such a two-step system for early stage rolling bearing failures in offshore wind turbines is introduced. First, empirical mode decomposition is applied to minimize the effect of ambient noise. Next, correlation coefficients between a reference signal and test signals are obtained and incipient fault detection is achieved by comparing the results with a threshold value. Through further analysis of the envelope spectrum, sample entropy for selected intrinsic mode functions is obtained, which is further used to train a support vector machine classifier to achieve fault classification and degradation state recognition. The proposed diagnostic approach is verified by experimental tests, and an accuracy of 98% in identifying and classifying rolling bearing failures under various loading conditions is obtained.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Date accepted:16 May 2021
Date deposited:22 June 2022
Date of first online publication:07 June 2021
Date first made open access:22 June 2022

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