Dispersion analysis for broadband guided wave using generalized warblet transform. (14th April 2016)

• Record Type: Journal Article
• Authors: Yang, Y.; Peng, Z.K.; Zhang, W.M.; Meng, G.; Lang, Z.Q.
• Is Part Of: Journal of sound and vibration. Volume 367(2016)
• Page Start: 22
• Page End: 36
• Publication Date: 2016-04-14
• Subjects: Broadband wave -- Highly dispersive -- Multi-modal wave -- Generalized warblet transform -- Mode isolation; Sound -- Periodicals; Vibration -- Periodicals; Son -- Périodiques; Vibration -- Périodiques; Sound; Vibration; Periodicals; Electronic journals; 620.205
• DOI: 10.1016/j.jsv.2015.12.037
• Languages: English
• ISSNs: 0022-460X

Abstract

Abstract: Dispersive properties of guided waves (GW), which indicate the group velocity of the wave varies with the frequency, have been widely investigated in many applications. The broadband GW is usually highly dispersive and multimodal, which is a good candidate for time–frequency analysis (TFA). In the time–frequency (TF) domain, the dispersion trajectory of a dispersive single-modal wave, which is a function of frequency, corresponds to its frequency-dependent dispersion law. To analyze such highly dispersive and multimodal broadband guided wave (HDMB-GW) effectively, we proposed a generalized warblet transform (GWT) based TFA method, which comprises the GWT and a mode separation procedure. Advantages of the proposed method include distinguishing and obtaining the dispersion trajectories of highly dispersive and overlapped modes of the HDMB-GW in the TF domain. Comparing with the existing TFA methods, the proposed method is more suitable for the HDMB-GW, especially when the dispersion trajectories of different modes intersect with each other. Both the simulated and experimental analysis on Lamb waves verified the effectiveness of the proposed method in the dispersion analysis for the HDMB-GW. Highlights: Generalized warblet transform and mode isolation are integrated for guided waves. Highly dispersive and overlapped dispersion trajectories can be distinguished. It outperforms other time-frequency methods for simulated and experimental signals.