A structural health monitoring strategy using cepstral features

A statistical pattern recognition based damage detection algorithm is proposed. The algorithm is developed according to the training and testing scheme, typical of pattern recognition applications. The original contribution of the work is given by the use of an adaptation of Mel-Frequency Cepstral Coefficients as damage sensitive features, as their compactness and de-correlation characteristics make them particularly suited for statistical pattern recognition applications. At the same time, the ease of extraction, which requires minimal user expertise, represents an important advantage over other more popular features, and makes the cepstral features particularly convenient for implementation into automatic structural health monitoring routines. The damage detection algorithm employs the squared Mahalanobis distance to solve the Structural Health Monitoring assignment. The method is validated by using both simulated and experimental data, and the performance of said features is compared to that of Auto-Regressive (AR) coefficients, which have been largely used to solve the task of structural damage detection. The experimental data were measured on a steel frame, which behave nonlinearly in its damaged configuration, at the Los Alamos National Laboratory. Results demonstrate that the proposed approach may be conveniently used in real-life applications, since cepstral features outperform AR coefficients when dealing with experimental data modeled to mimic the operational and environmental variability.