A structural health monitoring strategy using cepstral features

Balsamo, Luciana; Betti, Raimondo; Beigi, Homayoon

doi:10.1016/j.jsv.2014.04.062

Cited by 67 publications

(40 citation statements)

References 18 publications

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“…Obviously, this definition is very specific, perceptual, and suited for the aim of emulating the human ear. Nevertheless, this was found from previous studies to be apt for SHM purposes; this might be explained by its logarithmic-like spacing, which is recurrent in many natural events not limited to speech production and perception [21]. Another point of relevance is that the definition itself is perceptual, psychoacoustical, and based on perceived equidistance between subsequent pitches; thus, it is not directly provided with a mathematical formulation.…”

Section: Cepstral Coefficients (Ccs) and Mel-frequency Ccsmentioning

confidence: 91%

“…This case also presents its own difficulties, arising from the high flexibility of the cantilevered structure. The experimental data that are described in [21] have also been used for comparison. 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].…”

Section: Introductionmentioning

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: Cepstral Coefficients (Ccs) and Mel-frequency Ccsmentioning

confidence: 91%

Section: Introductionmentioning

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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“…Employing such a scale when dealing with the structural response of the system is not appropriate, mainly because the Mel-frequency scale was coined for a different scope and secondly because it is set up for a frequency range well over the ranges normally considered in civil engineering applications. For these reasons, the authors in [11] proposed to warp the linear frequency scale into a scale referred simply as the warped-frequency scale. The two scales are related through the following expression:…”

Section: Damage-sensitive Featurementioning

confidence: 99%

Data-based structural health monitoring using small training data sets

Balsamo

Betti

2015

Struct. Control Health Monit.

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SUMMARYOne of the most efficient ways to solve the damage detection problem using the statistical pattern recognition approach is that of exploiting the methods of outlier analysis. Cast within the pattern recognition framework, damage detection assesses whether the patterns of the damage-sensitive features extracted from the response of the system under unknown conditions depart from those drawn by the features extracted from the response of the system in a healthy state. The metric dominantly used to measure the testing feature's departure from the trained model is the Mahalanobis Squared Distance (MSD). Evaluation of the MSD requires the use of the inverse of the training population's covariance matrix. It is known that when the feature dimensions are comparable with the number of observations, the covariance matrix is ill-conditioned and numerically problematic to invert. When the number of observations is smaller than the feature dimensions, the covariance matrix is not even invertible. In this paper, four alternatives to the canonical damage detection procedure are investigated to address this issue: data compression through discrete cosine transform, use of pseudo-inverse of the covariance matrix, use of shrinkage estimate of the covariance matrix, and a combination of the three aforementioned techniques. The performance of the four methods is first studied on simulated data and then compared using the experimental data recorded on a fourstory steel frame excited at the base by means of a shaking table available at the Carleton Laboratory at Columbia University.

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“…Nonparametric‐statistical‐feature‐based damage detection methods are becoming increasingly popular for the health monitoring of structures. In this method, damage‐sensitive features are extracted from the measured vibration response (i.e., accelerations) of the undamaged structure, and the structure is monitored for any changes to these features . Zhang showed that by selecting a damage feature (DF) characterized as a random variable with a normal distribution, the probability of damage could be determined by comparing a feature of unknown condition with that of a known condition …”

Section: Introductionmentioning

confidence: 99%

Damage detection of steel girder railway bridges utilizing operational vibration response

Azim

Gül

2019

Struct Control Health Monit

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In this paper, we develop a damage identification framework based on acceleration responses for railroad bridges. The methodology uses sensor-clusteringbased time series analysis of bridge acceleration responses to the motion of the train. The results are expressed in terms of damage features, and damage to the bridge is investigated by observing the magnitude of these damage features. The investigation demonstrates the damage features by comparing the fit ratios of locations of interest so that damage can be identified and located and the relative severity of the damage assessed. The damage cases considered are stiffness loss, moment capacity reduction, and change in boundary conditions. In this study, a finite element analysis of a railway bridge model is used to verify our methodology. Our findings show that the proposed damage detection framework is very promising for continuously assessing the condition of railway bridges and thus will facilitate early detection of potential structural damage. This will be valuable for infrastructure owners seeking to develop more economical and effective maintenance strategies.

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A structural health monitoring strategy using cepstral features

Cited by 67 publications

References 18 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

Data-based structural health monitoring using small training data sets

Damage detection of steel girder railway bridges utilizing operational vibration response

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