Blade crack detection using variational model decomposition and time-delayed feedback nonlinear tri-stable stochastic resonance. (March 2023)

• Record Type: Journal Article
• Authors: Ma, Tianchi; Song, Di; Shen, Junxian; Xu, Feiyun
• Is Part Of: Structural health monitoring. Volume 22:Number 2(2023)
• Page Start: 1478
• Page End: 1493
• Publication Date: 2023-03
• Subjects: Blade crack detection -- amplitude modulation features -- variational mode decomposition (VMD) -- time-delay feedback nonlinear tri-stable stochastic resonance -- improved degree of cyclostationarity; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/14759217221109882
• Languages: English
• ISSNs: 1475-9217

Abstract

For centrifugal fans, the emergence of amplitude modulation (AM) components is a pivotal feature for blade crack fault. However, AM features extraction are challenging under strong interferences. A novel method is proposed to solve this problem using variational mode decomposition (VMD) and time-delayed feedback nonlinear tri-stable stochastic resonance (TFNTSR). Firstly, an improved degree of cyclostationarity (IDCS) index is constructed for parameters optimization. Then, intrinsic mode functions (IMFs) are obtained using the VMD method, and one of them which has the maximum IDCS value is selected. Finally, the selected IMF is further processed by the TFNTSR method, thereby completing the blade crack detection. Verified by simulations and experiments, VMD-TFNTSR method has a better performance on blade crack detection.