A singular value decomposition-based guided wave array signal processing approach for weak signals with low signal-to-noise ratios. (July 2020)

• Record Type: Journal Article
• Authors: Wang, Peng; Zhou, Wensong; Li, Hui
• Is Part Of: Mechanical systems and signal processing. Volume 141(2020)
• Publication Date: 2020-07
• Subjects: Singular value decomposition -- Array signal processing -- Guided wave -- Weak signal -- Low signal-to-noise ratio; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2019.106450
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: An SVD-based array signal processing method is proposed for weak signals. Numerical analyses and experiments show its advantages over traditional methods. The proposed method can be applied to the actual long-range detection. Abstract: This paper presents a novel singular value decomposition (SVD)-based guided wave array signal processing approach for relatively weak signals, which are usually encountered in long-range inspections. Because the scattered signals from a damaged area received by each element of a receiver array are from the same source, the damage information can be extracted from the covariance matrix of the array signals. By performing SVD of the covariance matrix of each part of the original array signals chosen by different time windows, the time of flight (ToF) of the damage-scattered signal corresponding to the largest singular value will be obtained. The effectiveness of the proposed SVD-based array signal processing method is then verified by a numerical analysis. Subsequently, experimental investigations are carried out on an orthotropic steel deck plate in an actual cable-stayed bridge and an 84-m-long steel pipe to verify the practicability of the SVD-based method. The results indicate that the proposed method is highly effective for array signal processing, especially for the signals with low signal-to-noise ratios (SNRs).