Detecting faults in structures using time-frequency techniques

We investigate various methods of classifying time-varying signals. In particular, we are interested in detecting acoustic emissions that may occur in concrete structures during imminent failure. This important classification problem results in detecting and separating the distress signal from other natural or man-made acoustic signals. Due to the time-varying nature of the signals, we employ several time-frequency-based classification methods proposed in the literature. We also propose a new automatic classification method that is based on the matching pursuit algorithm, and we demonstrate its superior performance using real data.