TABLE OF CONTENTS This chapter examines two aspects of considerable importance to the structural health monitoring (SHM) process: signal processing and damage identification/pattern recognition algorithms. The SHM community is constantly exposed to various aspects of signal processing, spectra collection, data processing and analysis, pattern recognition, and decision making. This chapter will review some of these aspects. Both the state- of-the-art and general principles, on one hand, and specific examples, on the other hand, will be presented. However, an exhaustive presentation of these vast topics will not be attempted, since it would be beyond the scope and possibilities of a single chapter.
The signal-processing subject is described in terms of two major algorithmic paths: the short-term Fourier transform (STFT) and the wavelet transform (WT). Both these approaches fall into the larger class of time-frequency analysis. Their use is to gain a better understanding of the signal spectrum time-wise behavior and, based on this, extract that part (or bandwidth) of the signal that is of relevance to the SHM process. In many instances, the signal processing can be used as a pre- or post-processing action that enhances the efficiency of SHM techniques presented in previous chapters.
This damage identification/pattern recognition process will be discussed in the context of the analysis of spectrum features (e.g. resonance frequencies, resonance peaks). The existence of adequate algorithms would allow one to classify the spectral data into classes according to the damage state of the structure. An elementary classification problem would consist of distinguishing between...
