Acoustic-based whistle detection of drain hole for wind turbine blade. (December 2022)

• Record Type: Journal Article
• Authors: Chen, Bin; Zhang, Minghao; Lin, Zhankun; Xu, Hao
• Is Part Of: ISA transactions. Volume 131(2022)
• Page Start: 736
• Page End: 747
• Publication Date: 2022-12
• Subjects: Wind turbine blade -- Acoustical detection -- Whistle event -- Drain hole -- Correlated empirical mode decomposition -- Feature extraction; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2022.05.010
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: The blade is a crucial part of large-scale wind turbine for converting wind energy into electricity. Increasing attentions have been paid to the blade health monitoring in recent years. This paper focuses on acoustic-based surface damage detection of the blade and presents a novel intelligent detection method of the whistle produced by the drain hold using the pattern recognition. In the algorithm, a developed preprocessing strategy with multiband adaptive spectral subtraction can well reduce the random wind noise from raw acoustic signal. Moreover, a correlated empirical mode decomposition method in combination with morphological filtering is proposed for extracting time–frequency ridge features of whistle event. Finally, an incremental support vector machine based on adaptive reserved set strategy is designed for recognizing the whistle event. Experimental results demonstrate that proposed method is feasible and effective in whistle detection of drain hole. Highlights: A novel acoustic-based whistle detection method of drain hole is proposed. The whistle characteristics of drain hole for wind turbine blade is investigated. An incremental AR-ISVM with correlated EMD is designed for recognizing the whistle. Experiments validate effectiveness of proposed method in whistle detection.