![]() This is performed by training a “normality” model via ML techniques over the features that were extracted from the known set of data. The concept is simple: by knowing the current state of a system (which is usually undamaged, but can be indistinctly previously damaged, as long as it is stable and not dramatically changing under “normal” conditions), a baseline can be defined. In this context, recent years have seen a continuous surge of interest in Machine Learning (ML) algorithms, as they are perfectly suited for damage detection, if this is seen from a Pattern Recognition/Novelty Detection standpoint. More specifically, vibration-based SHM deals with the structural response, as recorded by means of a sensor network, to extract damage-related features from its free or forced oscillations. Promising results from both numerical and experimental data were obtained.ĭamage detection and Structural Health Monitoring (SHM) indicate the field of engineering that is involved in the assessment of the structural integrity for civil, mechanical, and aerospace applications, among others. The goal of this paper is to introduce the use of TECCs for damage detection purposes, by highlighting their competitiveness with closely related features. These features are very closely related to MFCCs, but provide interesting and useful additional values, such as e.g., improved robustness with respect to noise. ![]() The Teager-Kaiser Energy Cepstral Coefficients (TECCs) is one of these alternatives. This approach, while efficient and still very common in applications, such as speech and speaker recognition, has been followed by other more advanced and competitive techniques for the same aims. Previous works relied on the Mel-Frequency Cepstral Coefficients (MFCCs). ![]() ![]() In particular, the Cepstral Coefficients (CCs) proved to be apt in discerning the response of a damaged structure with respect to a given undamaged baseline. Recently, features and techniques from speech processing have started to gain increasing attention in the Structural Health Monitoring (SHM) community, in the context of vibration analysis. ![]()
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