On time-frequency domain feature extraction of wave signals for structural health monitoring

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.