Using Enhanced Cepstral Analysis for Structural Health Monitoring

Ferraris, Matteo; Civera, Marco; Ceravolo, Rosario; Surace, Cecilia; Betti, Raimondo

doi:10.1007/978-981-13-8331-1_11

Cited by 12 publications

(20 citation statements)

References 17 publications

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“…Some of these alternatives, such as the Bark-Frequency Cepstral Coefficients (BFCCs) [54], have also been recently tested by the Authors for SHM purposes. In this context, three novel scales, namely, the Energy COntent-based MMFCCs (ECO-MMFCCs), the MOdal Frequency-centred MMFCCs (MO-MMFCCs), and the LOw-Frequency Restricted MMFCCs (LO-MMFCCs) have been recently proposed [22].…”

Section: Similar Scales and Features Investigatedmentioning

confidence: 99%

“…Signal pre-processing on the acceleration THs was executed, as indicated in [22]. For s acquisition channels, n tr realisation and thus n tr •s THs for training the ML algorithm, it is then possible to extract n tr feature vectors, i.e., c (i) ∈ R s•(L−1)×1 , where i = 1, .…”

Section: Damage Detection Algorithmmentioning

confidence: 99%

“…The Finite Element Model (FEM) of the Santa Maria and San Giovenale Cathedral bell tower (as visible in Figure 4) has been used as a numerical case study for the proposed damage-sensitive feature, as previously proposed for similar alternatives in [22]. The baseline FE Model of the structure 'as is' has been calibrated accordingly to the results of a campaign of ambient vibration tests [29], aimed to establish the mechanical properties of the deteriorated (or cracked) masonry walls.…”

Section: Santa Maria and San Giovenale Cathedral Bell Towermentioning

confidence: 99%

“…The baseline FE model was modified to obtain several damage scenarios. These are, again for comparison' sake, the same as utilised in [22]. In all cases, the damage was simulated as a reduction of the Young's modulus E at given locations to emulate damage in the masonry external walls.…”

Section: Santa Maria and San Giovenale Cathedral Bell Towermentioning

confidence: 99%

“…In this last case study, the damage is modelled as a breathing crack mechanism, introducing a source of nonlinearities often encountered in damaged structures, where the presence of crack often results in nonlinear behaviour [34,35]. As a benchmark, the original five alternative methods that were proposed in [21] and the six new closely-related variants described in [22] have also been applied to the numerical and experimental data; the results show interesting improvements.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Similar Scales and Features Investigatedmentioning

confidence: 99%

Section: Damage Detection Algorithmmentioning

confidence: 99%

Section: Santa Maria and San Giovenale Cathedral Bell Towermentioning

confidence: 99%

Section: Santa Maria and San Giovenale Cathedral Bell Towermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Recursive partitioning and Gaussian Process Regression for the detection and localization of damages in pultruded Glass Fiber Reinforced Polymer material

Boscato

Civera

Fragonara

2021

Struct Control Health Monit

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In this paper, a methodology for the detection and localization of damages in composite pultruded members is proposed. This is particularly relevant to thin-walled pultruded members, which are typically characterized by orthotropic behavior, anisotropic along the fibers and isotropic in the cross section. Hence, a method to detect and localize damage, and the influence these might have on the performance of thin-walled Glass Fiber Reinforced Polymer (GFRP) members, is proposed and applied to both numerical and experimental data. Specifically, the numerical and experimental modal shapes of a narrow flange pultruded profile are analyzed. The reliability of the proposed semiparametric statistical method, which is based on Gaussian Processes Regression and Bayesian-based Recursive Partitioning, is analyzed on a narrow flange profile, artificially affected by sawed notches with incremental depth.The numerical investigation is carried out via finite element models (FEMs) of the cracked beam, where the dynamic parameters and the modal shapes are computed. In total, three different crack sizes are investigated, to compare the results with the experimental ones. Finally, the proposed approach is further extended and validated on numerically simulated frame structures.

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Filter Banks and Hybrid Deep Learning Architectures for Performance-Based Seismic Assessments of Bridges

Sajedi

Liang

2022

J. Struct. Eng.

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Using Enhanced Cepstral Analysis for Structural Health Monitoring

Cited by 12 publications

References 17 publications

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

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

Recursive partitioning and Gaussian Process Regression for the detection and localization of damages in pultruded Glass Fiber Reinforced Polymer material

Filter Banks and Hybrid Deep Learning Architectures for Performance-Based Seismic Assessments of Bridges

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