Structural damage assessment through features in quefrency domain. (15th January 2021) - Morgantini, Marcello; Betti, Raimondo; Balsamo, Luciana
Highlights: Statistical pattern recognition identifies patterns to assess the presence of damage. Features in quefrency domain are analytically derived from structural properties. PCA projection increases features robustness to external distrubances. New features are computationally advantageous compared to natural frequencies. Proposed damage assessment strategy is tested with numerical and experimental data. Abstract: The difficulties in dynamic system modeling and the recently developed machine learning tools have moved the attention of the structural damage assessment community towards a new direction. Attention is now placed on the immediate extraction of damage sensitive features directly from the monitored dynamic response of the structure, allowing engineers by-pass the creation of complex and sophisticated structural models. It is in this framework that the work presented in this paper has to be considered: the extraction of new damage sensitive features in the quefrency domain is discussed in the context of a vibration based analysis. Despite such a domain has been fully explored in acoustics, it represents an uncommon approach for the civil engineering community. In this paper, some variables characterizing the system dynamic response in the quefrency domain, referred to as cepstral coefficients, have been derived as functions of the structural characteristics and used in a damage assessment strategy. The main advantage of the quefrency domain over the frequency domain consists in a massive dimensionality reduction and, when dealing with real data, in a simplification of the extraction of cepstral coefficients versus that of natural frequencies. In this paper, the analytical expression of cepstral coefficients of the structural acceleration response has been derived and the statistical distribution of such coefficients has been investigated in a pattern recognition approach. In addition, a damage assessment strategy has been proposed by extracting, through a Principal Component Analysis (PCA), the projections of the cepstral coefficients with the lowest variances so to minimize the effect of external disturbances. The effectiveness of the proposed damage assessment strategy has been validated through numerical simulations and experimental data.