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Open AccessFeature PaperArticle

The Teager-Kaiser Energy Cepstral Coefficients as an Effective Structural Health Monitoring Tool

1
Department of Mechanical and Aerospace Engineering-DIMEAS, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
2
Department of Structural, Geotechnical and Building Engineering-DISEG, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
3
Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(23), 5064; https://doi.org/10.3390/app9235064
Received: 22 October 2019 / Revised: 14 November 2019 / Accepted: 15 November 2019 / Published: 23 November 2019
(This article belongs to the Special Issue Nondestructive Testing (NDT))
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. 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. Previous works relied on the Mel-Frequency Cepstral Coefficients (MFCCs). 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. The Teager-Kaiser Energy Cepstral Coefficients (TECCs) is one of these alternatives. 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 goal of this paper is to introduce the use of TECCs for damage detection purposes, by highlighting their competitiveness with closely related features. Promising results from both numerical and experimental data were obtained. View Full-Text
Keywords: damage detection; cepstral analysis; Structural Health Monitoring; Machine Learning; Teager-Kaiser Energy; gammatone filter damage detection; cepstral analysis; Structural Health Monitoring; Machine Learning; Teager-Kaiser Energy; gammatone filter
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MDPI and ACS Style

Civera, M.; Ferraris, M.; Ceravolo, R.; Surace, C.; Betti, R. The Teager-Kaiser Energy Cepstral Coefficients as an Effective Structural Health Monitoring Tool. Appl. Sci. 2019, 9, 5064.

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