A data-driven group-sparse feature extraction method for fault detection of wind turbine transmission system. (27th April 2020)

• Record Type: Journal Article
• Authors: He, Wangpeng; Guo, Baolong; Chen, Binqiang; Ye, Junjie; Bechhoefer, Eric
• Is Part Of: Measurement science & technology. Volume 31:Number 7(2020)
• Publication Date: 2020-04-27
• Subjects: wind turbine fault detection -- vibration sensors -- condition monitoring -- periodic-group-sparse features -- adaptively regularized POGS; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ab79c9
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Vibration monitoring using sensors mounted on machines is widely used for rotating machinery fault diagnosis. The periodic overlapping group sparsity (POGS) method has been developed in previous work of authors, and is an effective technique for detecting faults induced in rotating machines. However, the regularization parameter of the POGS problem is roughly specified via a look-up table provided in the original work. To address this problem, a data-driven diagnostic method, which is termed the adaptively regularized periodic overlapping group sparsity (ARPOGS), is proposed in this paper. The non-stationary fault feature ratio which is defined in the Hilbert domain is employed to guide the optimal regularization parameter. The criterion of setting the interval of candidate regularization parameters is also discussed. The ARPOGS is developed in terms of a convex optimization problem, while non-convex regularizations are used to further promote the sparsity. Since the non-convex penalty term is used and the whole objective function is constrained as a convex optimization problem, the sparsity of useful fault features is maximally induced. A simulated signal is formulated to verify the performance of the proposed method for periodic feature extraction. Finally, the effectiveness of the proposed ARPOGS method is validated by analyzing real data collected from a wind turbine transmission system. The results demonstrated that the proposed method can effectively and automatically extract periodic-group-sparse features from noisy vibration signals.