Early Fault Diagnostic System for Rolling Bearing Faults in Wind Turbines

Xu, Libowen; Wang, Qing; Ivrissimtzis, Ioannis; Li, Shisong

Abstract

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.

Citation

Xu, L., Wang, Q., Ivrissimtzis, I., & Li, S. (2022). Early Fault Diagnostic System for Rolling Bearing Faults in Wind Turbines. Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, 5(1), Article 011004. https://doi.org/10.1115/1.4051222

• Journal Article Type: Article
• Acceptance Date: May 16, 2021
• Online Publication Date: Jun 7, 2021
• Publication Date: 2022-02
• Deposit Date: Nov 1, 2021
• Publicly Available Date: Jun 22, 2022
• Journal: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
• Print ISSN: 2572-3901
• Electronic ISSN: 2572-3898
• Publisher: American Society of Mechanical Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Issue: 1
• Article Number: 011004
• DOI: https://doi.org/10.1115/1.4051222
• Public URL: https://durham-repository.worktribe.com/output/1223876