On time-frequency domain feature extraction of wave signals for structural health monitoring. (January 2018)

• Record Type: Journal Article
• Authors: Lu, Yi; Tang, Jiong
• Is Part Of: Measurement. Volume 114(2018)
• Page Start: 51
• Page End: 59
• Publication Date: 2018-01
• Subjects: Structural health monitoring -- Feature extraction -- Time-frequency domain analysis -- Decision making -- Lamb wave; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2017.09.016
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Feature extraction for wave-propagation based structural health monitoring is investigated. A method based on the adaptive harmonic wavelet transform (AHWT) is formulated and executed. Case study shows the AHWT is a simple and viable option to facilitate robust decision making. Abstract: Wave propagation signals are commonly used as information carrier in structural health monitoring. To facilitate decision making, wave signals are often decomposed into multiple components to reveal its frequency or time-frequency content. In this research we investigate the use of time-frequency decomposition techniques for feature extraction. The method based on the adaptive harmonic wavelet transform (AHWT) possesses high computational efficiency and at the same time inherently avoids certain issues in some other time-frequency feature extraction methods, e.g., the empirical mode decomposition (EMD). EMD entertains the adaptivity with respect to signal features, but that adaptive nature affects the comparability of the resulting intrinsic mode functions (IMFs). In contrast, the AHWT based method has similar feature extraction capabilities in the time-frequency domain while using a deterministic basis. Therefore, wavelet features can become cross-comparable when common wavelet basis is used. Our case study shows that the AHWT based approach can identify the critical features in Lamb wave signals to realize effective and robust decision making in structural damage detection.