Damage detection of bridges based on combining efficient cepstral coefficients

Abstract

In this article, a novel vibration-based damage detection approach is proposed based on selecting effective cepstral coefficients, consisting of three main stages: (1) signal processing and feature extraction, (2) damage detection by combining effective cepstral coefficients through feature selection methods, and (3) performance evaluation. First, two feature extraction techniques are used in damage identification systems, including linear prediction cepstral coefficients and mel frequency cepstral coefficients. Second, to improve the performance of damage detection, the combination of the effective cepstral coefficients is proposed as a damage index. By applying several feature selection methods, the most effective coefficients are found and then combined to create a subset that carries the most significant information about the structural damage. Finally, the support vector machine classifier is performed to evaluate the proposed approach in detecting the structural damage. The proposed technique is verified using a suite of numerical and full-scale studies. Results confirm that the proposed method achieves a significant performance with great accuracy and reduces false alarms.

A Feature Extraction & Selection Benchmark for Structural Health Monitoring

“…50 Cepstrum analysis has shown promise as an efficient measure for detecting changes in a signal that are not easily identified in the spectrum. 79 In Ref. [37], the link between cepstrum based features and modal properties of a structure are defined with cepstrum features found to be an effective measure of damage detection.…”

“…The LPCC can be calculated aswhere am is the linear prediction coefficient, Cm is the cepstral coefficient and m is the number of samples in an analysis frame/window. 79…”

“…where a m is the linear prediction coefficient, C m is the cepstral coefficient and m is the number of samples in an analysis frame/window. 79 Wavelet Transform. The continuous wavelet transform (CWT) is an effective spectral feature for providing smooth and specific time frequency resolution throughout a signal The STFT is limited in that the fixed-length window leads to a fixed time-frequency resolution across the whole time-frequency domain.…”

Crack localization in glass fiber composite beams by experimental modal analysis and multi variable Gaussian process regression method

“…Vibration-based structural health monitoring has emerged as a crucial tool for effectively locating cracks in both rotating machines and composite structures (Pacheco et al. , 2021; Bisheh et al. , 2021).…”

“…These modes were further analyzed using the modal assurance criterion. Bisheh et al (2021) developed an innovative vibration-based damage detection method, enhancing performance by incorporating effective cepstral coefficients as a damage index, a validation achieved through numerical studies. Suryateja et al (2020) predicted crack locations in propeller shafts by intersecting the first three mode shapes obtained from natural frequency tests, exhibiting substantial agreement between analytical and experimental results.…”