Structural Health Monitoring Community Research Articles

Attention mechanism enhanced spatiotemporal-based deep learning approach for classifying barely visible impact damages in CFRP materials

Most existing machine learning approaches for analysing thermograms mainly focus on either thermal images or pixel-wise temporal profiles of specimens. To fully leverage useful information in thermograms, this article presents a novel spatiotemporal-based deep learning model incorporating an attention mechanism. Using captured thermal image sequences, the model aims to better characterise barely visible impact damages (BVID) in composite materials caused by different impact energy levels. This model establishes the relationship between patterns of BVID in thermography and their corresponding impact energy levels by learning from spatial and temporal information simultaneously. Validation of the model using 100 composite specimens subjected to five different low-velocity impact forces demonstrates its superior performance with a classification accuracy of over 95%. The proposed approach can contribute to Structural Health Monitoring (SHM) community by enabling cause analysis of impact incidents based on predicting the potential impact energy levels. This enables more targeted predictive maintenance, which is especially significant in the aviation industry, where any impact incidents can have catastrophic consequences.

Damage identification of long-span bridges based on the correlation of monitored global dynamic responses in high dimensional space

Structural damage identification and condition assessment is the most direct goal in structural health monitoring (SHM). Vibration-based methods (VBM) using the global dynamic responses (displacement or acceleration), including modal-based methods and statistical methods, have been comprehensively investigated in the last few decades in the SHM community. These two types of methods face challenges in practice due to the indicators being sensitive to the environment and insensitive to local damage. It is curious whether new features of data in high-dimension space can be found and the new features can be used as damage indicators. This study proposes a method based on the correlation of the global dynamic responses in high dimensional space for damage identification. It is proved that the ratio of mean square (RoMS) between any two responses (two-dimensional space) is dependent on the modal parameters of the structure only and changes only with the damage of the structures. Therefore, it can be used as a damage indicator. The proposed method is validated through a finite element numerical analysis on a cable-stayed bridge and the monitored acceleration of a real-world suspension bridge, and is further compared with the modal analysis methods. The results show that the proposed method can accurately identify the damage and is sensitive to local damage compared to modal parameters.

Modal analysis under jittering and kernel clock distribution: single-output identification

Smartphones have attracted attention in the structural health monitoring community due to their embedded multisensory platforms suitable for crowdsourcing innovation. Despite their advantages, smartphone sensors are originally designed for user utility rather than technical engineering applications (e.g. vibration analysis and modal identification). Sampling jitter is among those problems adversely influencing smartphone performance as a scientific device that can be used for characterising civil infrastructure dynamic characteristics. With inconsistencies in the sampling period due to jitter effects, the identified modal frequencies of a structure can deviate from the actual value, which is artificially introduced by smartphone clock errors. In this study, the authors formulate the statistical characteristics of the smartphone sampling period through kernel distribution and apply a digital reconstruction remedy to the sampling jitter problem. Through introductory simulations of a sine wave and physical implementations through shaking table tests equipped with smartphones, the efficiency of kernel distribution diagnosis and the signal-reconstruction remedy is presented. Following the simulation and laboratory applications, the proposed techniques are applied to vibration monitoring of a steel pedestrian bridge in terms of the auto power spectral density and short-time Fourier transform of single-output signals. The results show successful rehabilitation of accelerometer data from smartphones, removing jitter-induced errors to a significant extent and accordingly improving the identification accuracy currently in a single-output setting.

A Comparative Study on Crack Detection in Concrete Walls Using Transfer Learning Techniques

Structural cracks have serious repercussions on the safety, adaptability, and longevity of structures. Therefore, assessing cracks is an important parameter when evaluating the quality of concrete construction. As numerous cutting-edge automated inspection systems that exploit cracks have been developed, the necessity for individual/personal onsite inspection has reduced exponentially. However, these methods need to be improved in terms of cost efficiency and accuracy. The deep-learning-based assessment approaches for structural systems have seen a significant development noticed by the structural health monitoring (SHM) community. Convolutional neural networks (CNNs) are vital in these deep learning methods. Technologies such as convolutional neural networks hold promise for precise and accurate condition evaluation. Moreover, transfer learning enables users to use CNNs without needing a comprehensive grasp of algorithms or the capability to modify pre-trained networks for particular purposes. Within the context of this study, a thorough analysis of well-known pre-trained networks for classifying the cracks in buildings made of concrete is conducted. The classification performance of convolutional neural network designs such as VGG16, VGG19, ResNet 50, MobileNet, and Xception is compared to one another with the concrete crack image dataset. It is identified that the ResNet50-based classifier provided accuracy scores of 99.91% for training and 99.88% for testing. Xception architecture delivered the least performance, with training and test accuracy of 99.64% and 98.82%, respectively.

Deep autoencoder model for direct monitoring of bridges subjected to a moving vehicle load under varying temperature conditions

Structural health monitoring (SHM) approaches have offered potential solutions for monitoring and analyzing the behavior of transport infrastructures. Recently, vibration-based damage detection using deep learning approaches has received considerable attention in the SHM community. Previous studies on structural damage detection have employed supervised deep learning models, which require large amounts of labeled data. However, acquiring labeled data in practical engineering is challenging, costly, and sometimes impractical. This study uses a deep autoencoder model to extract the damage-sensitive features from acceleration data in the frequency domain without any labels. For this, a numerical example of a concrete highway bridge model subjected to a single-vehicle load under varying temperatures, low-extent damages, vehicle speeds, road surface conditions, and measurement noises was used to evaluate the effectiveness of the proposed method. This study demonstrated that the trained model is sensitive to damage in terms of reconstruction loss. In addition, the damage index (DI) between different damage-sensitive features was calculated using the Gaussian process-based z-scores. The results show that the proposed method has good damage detection capability, with the model struggling only at a higher speed (V3 = 8.33 m/s) with poor road surface roughness, where damage becomes evident after 10% or 15% damage severity. These results emphasize the potential of using the proposed method in practical engineering.

Adaptive Fault Diagnosis for Simultaneous Sensor Faults in Structural Health Monitoring Systems

Structural health monitoring (SHM) is a non-destructive testing method that supports the condition assessment and lifetime estimation of civil infrastructure. Sensor faults may result in the loss of valuable data and erroneous structural condition assessments and lifetime estimations, in the worst case with structural damage remaining undetected. As a result, the concepts of fault diagnosis (FD) have been increasingly adopted by the SHM community. However, most FD concepts for SHM consider only single-fault occurrence, which may oversimplify actual fault occurrences in real-world SHM systems. This paper presents an adaptive FD approach for SHM systems that addresses simultaneous faults occurring in multiple sensors. The adaptive FD approach encompasses fault detection, isolation, and accommodation, and it builds upon analytical redundancy, which uses correlated data from multiple sensors of an SHM system. Specifically, faults are detected using the predictive capabilities of artificial neural network (ANN) models that leverage correlations within sensor data. Upon defining time instances of fault occurrences in the sensor data, faults are isolated by analyzing the moving average of individual sensor data around the time instances. For fault accommodation, the ANN models are adapted by removing faulty sensors and by using sensor data prior to the occurrence of faults to produce virtual outputs that substitute the faulty sensor data. The proposed adaptive FD approach is validated via two tests using sensor data recorded by an SHM system installed on a railway bridge. The results demonstrate that the proposed approach is capable of ensuring the accuracy, reliability, and performance of real-world SHM systems, in which faults in multiple sensors occur simultaneously.

A literature review: Generative adversarial networks for civil structural health monitoring

Structural Health Monitoring (SHM) of civil structures has been constantly evolving with novel methods, advancements in data science, and more accessible technology to address issues related to structural safety, operations, and resiliency. Research and development in the civil SHM field during the last few decades have been progressive due to the increasing use of Artificial Intelligence (AI) methods such as Machine Learning (ML) and Deep Learning (DL). Particularly, Generative Adversarial Networks (GAN), which is a subfamily of Deep Learning has been highly favored in the SHM community within the last couple of years. After its release in 2014, GANs (original GAN and other GAN variants) have been in use for a wide variety of applications in various disciplines, and it has been one of the most popular research topics in the AI-ML domain. While there has not been a review study on the applications of GAN in the civil SHM field, this paper aims to fill this gap by presenting a literature review of the studies that employed GAN specifically in civil SHM applications from 2014 to date, in a condensed format. This study intends to inform SHM practitioners and researchers about GANs and present the highlights of the published work on GANs in the civil SHM field.

Damage identification of long-span bridges based on the correlation of probability distribution of monitored quasi-static responses

Structural health diagnosis is one of the most critical issues in structural health monitoring (SHM). Vibration-based methods have been extensively investigated in the last few decades in the SHM community, but they encounter the challenges of local damage insensitivity and environmental sensitivity. However, the study of structural health diagnosis based on structural quasi-static response data, which has well-defined spatial coordinates and is sensitive to the local condition of the structure, is insufficient. It is difficult to extract structural models or parameters that directly indicate structural damage owing to the coupling effect of the damage and unknown external loads in the quasi-static response data. This study proposes a method based on the correlation of the probability distribution of the quasi-static response data for damage identification, and it is proved that the variation in the structural condition can be inferred from the variation in the correlation of the probability distribution of multiple quasi-static response data. The earth mover’s distance (EMD) is introduced as a quantitative indicator of the variation in the probability distribution function. The difference in the EMD (DEMD) between two monitored quasi-static responses was adopted as the damage indicator. The monitored strain on the steel box girder of a multi-tower cable-stayed bridge and the monitored cable tension of the cable-stayed bridge were employed to validate the proposed method. The results show that the proposed method successfully identifies the damage to the steel box girders and stay cables.

Assessment of Transfer Learning Capabilities for Fatigue Damage Classification and Detection in Aluminum Specimens with Different Notch Geometries

Fatigue damage detection and its classification in metallic materials are persistently challenging the structural health monitoring community. The mechanics of fatigue damage is difficult to analyze and is further complicated because of the presence of notches of different geometries. These notches act as possible crack-nucleation sites resulting in failure mechanisms that are drastically different from one another. Often, sensor-based tools are used to monitor and detect fatigue damage in critical metallic materials such as aluminum alloys. Through deep neural networks (DNNs), such a sensor-based approach can be ubiquitously extended for a variety of geometries as appropriate for different applications. To that end, this paper presents a DNN-based transfer learning framework that can be used to classify and detect fatigue damage across candidate notch geometries. The DNNs are built upon ultrasonic time-series data obtained during fatigue testing of Al7075-T6 specimens with two types of notch geometries, namely, a U-notch and a V-notch. The baseline U-notch DNN is shown to achieve an accuracy of 96.1% while the baseline V-notch DNN has an accuracy of 95.8%. Both baseline DNNs are, thereafter, subjected to a transfer learning process by keeping a certain number of layers frozen and retraining only the remaining layers with a small volume of data obtained from the other notch geometry. When a layer of the baseline U-notch DNN is retrained with just 10% of the total V-notch data, an accuracy above 90% is observed for fatigue damage detection of V-notch specimens. Similar results are also obtained when the baseline V-notch DNN is retrained and interrogated to detect damage for U-notch specimens. These results, in summary, demonstrate the data-thrifty quality of combining the concepts of transfer learning and DNN for fatigue damage detection in different geometries of specimens made of high-performance aluminum alloys.

Data Augmentation for Deep-Learning-Based Multiclass Structural Damage Detection Using Limited Information.

The deterioration of infrastructure’s health has become more predominant on a global scale during the 21st century. Aging infrastructure as well as those structures damaged by natural disasters have prompted the research community to improve state-of-the-art methodologies for conducting Structural Health Monitoring (SHM). The necessity for efficient SHM arises from the hazards damaged infrastructure imposes, often resulting in structural collapse, leading to economic loss and human fatalities. Furthermore, day-to-day operations in these affected areas are limited until an inspection is performed to assess the level of damage experienced by the structure and the required rehabilitation determined. However, human-based inspections are often labor-intensive, inefficient, subjective, and restricted to accessible site locations, which ultimately negatively impact our ability to collect large amounts of data from inspection sites. Though Deep-Learning (DL) methods have been heavily explored in the past decade to rectify the limitations of traditional methods and automate structural inspection, data scarcity continues to remain prevalent within the field of SHM. The absence of sufficiently large, balanced, and generalized databases to train DL-based models often results in inaccurate and biased damage predictions. Recently, Generative Adversarial Networks (GANs) have received attention from the SHM community as a data augmentation tool by which a training dataset can be expanded to improve the damage classification. However, there are no existing studies within the SHM field which investigate the performance of DL-based multiclass damage identification using synthetic data generated from GANs. Therefore, this paper investigates the performance of a convolutional neural network architecture using synthetic images generated from a GAN for multiclass damage detection of concrete surfaces. Through this study, it was determined the average classification performance of the proposed CNN on hybrid datasets decreased by 10.6% and 7.4% for validation and testing datasets when compared to the same model trained entirely on real samples. Moreover, each model’s performance decreased on average by 1.6% when comparing a singular model trained with real samples and the same model trained with both real and synthetic samples for a given training configuration. The correlation between classification accuracy and the amount and diversity of synthetic data used for data augmentation is quantified and the effect of using limited data to train existing GAN architectures is investigated. It was observed that the diversity of the samples decreases and correlation increases with the increase in the number of synthetic samples.

Damage localization and characterization using one-dimensional convolutional neural network and a sparse network of transducers

Early damage identification and continuous system monitoring save dramatically maintenance costs and increase the lifespan of priceless structures. Convolutional neural networks (CNNs) have attracted the attention of the structural health monitoring (SHM) community in recent years due to their great potential for identifying underlying data patterns. However, employing two-dimensional convolutional layers in a CNN necessitates the use of strong computing resources. Therefore, based on the present state-of-the-art technical solutions, a two-dimensional CNN is not suitable for real-time SHM applications with stand-alone processing units. One-dimensional convolutional networks (1D-CNN) have recently been employed in Ultrasonic Guided Wave-based (UGW-based) damage detection to address the aforementioned disadvantage. In this paper, a methodology for damage assessment at three levels – detection, localization, and characterization – based on 1D-CNN is put forward. Furthermore, the sequence length of the time-domain signals is significantly shortened by the application of a novel approach for processing them. Additionally, it is shown to what extend this method can improve the distinguishability between datapoints obtained from various damage scenarios. Consequently, by reducing the dimensionality of the problem, the proposed approach significantly reduces the memory usage of the classification algorithm. Experimental measurements as well as Numerical simulations, in which various damage scenarios such as corrosion, circular hole and cracks have been considered, are carried out to evaluate the efficacy of the proposed algorithm. It is shown that the suggested approach has benefits in terms of true classification rate of instances (above 93 percent for detection, localization, and characterization), computing time, in-situ monitoring, and noise resilience.

A change-point detection method for detecting and locating the abrupt changes in distributions of damage-sensitive features of SHM data, with application to structural condition assessment

Damage detection or structural condition assessment is an important objective of structural health monitoring (SHM). The damages or adverse changes in structural conditions can usually be manifested as pattern changes in damage-sensitive features (DSFs) extracted from SHM data; this enables us to shift damage detection to DSF change detection. Online monitoring can accumulate huge amounts of data, finding the changes from the massive DSF data through manual inspection is impractical; thus, automatic detection tools are required. If possible, relevant significance test is also desired to make a rational judgment on the existence of a change. In this sense, the change-point detection technique is an attractive choice, which is increasingly proved to be a powerful change detection tool in various SHM applications. However, existing change-point detection methods in SHM are mainly used for scalar or vector data, thus incapable of detecting changes in features represented by complex data, for example, the probability density functions (PDFs). Detecting abrupt changes in the distributions (represented by PDFs) of the DSF data is of crucial concern in structural condition assessment. However, relevant automatic diagnostic tools have not been well developed in the SHM community. To this end, a novel change-point detection method is developed in the functional data analytic framework for this task. The proposed approach has advantages in detecting changes for massive data and directly handling general PDFs. Considering that the major challenge in PDF-valued data analysis comes from the nonlinear constraints of PDFs, the PDFs are embedded into the Bayes space to develop the detection methodology by using the linear structure of the Bayes space. Comprehensive simulation studies are conducted to validate the effectiveness of the proposed method as well as demonstrate its superiority over the competing method. Finally, a case study involving cable condition assessment of a long-span bridge demonstrates its practical utility in SHM.

Domain Adaptation for Structural Fault Detection under Model Uncertainty

In the last decade, the interest in machine learning (ML) has grown significantly within the structural health monitoring (SHM) community. Traditional supervised ML approaches for detecting faults assume that the training and test data come from similar distributions. However, real-world applications, where an ML model is trained, for example, on numerical simulation data and tested on experimental data, are deemed to fail in detecting the damage. The deterioration in the prediction performance is mainly related to the fact that the numerical and experimental data are collected under different conditions and they do not share the same underlying features. This paper proposes a domain adaptation approach for ML-based damage detection and localization problems where the classifier has access to the labeled training (source) and unlabeled test (target) data, but the source and target domains are statistically different. The proposed domain adaptation method seeks to form a feature space that is capable of representing both source and target domains by implementing a domain-adversarial neural network. This neural network uses H-divergence criteria to minimize the discrepancy between the source and target domain in a latent feature space. To evaluate the performance, we present two case studies where we design a neural network model for classifying the health condition of a variety of systems. The effectiveness of the domain adaptation is shown by computing the classification accuracy of the unlabeled target data with and without domain adaptation. Furthermore, the performance gain of the domain adaptation over a well-known transfer knowledge approach called Transfer Component Analysis is also demonstrated. Overall, the results demonstrate that the domain adaption is a valid approach for damage detection applications where access to labeled experimental data is limited.

Uncertainty quantification for the distribution-to-warping function regression method used in distribution reconstruction of missing structural health monitoring data

Data loss is a common problem of structural health monitoring and adversely affects many structural health monitoring applications. Tremendous progress in missing structural health monitoring data imputation has been made in recent years, forming an important part of sensor validation. Most of the imputed data are based on estimates obtained by data-driven statistical or machine learning models; quantifying their estimation uncertainties is significant in terms of being able to perform accuracy assessments and providing more insights into the imputed data. However, this procedure has been surprisingly neglected in the structural health monitoring community. This article focuses on uncertainty quantification for the distribution-to-warping function regression method (an indirect distribution-to-distribution regression method) used in reconstructing distributions of missing data. The distribution-to-warping function regression method belongs to the framework of functional data analysis as both the predictor and response are continuous functions. The challenge of performing uncertainty quantification for the distribution-to-warping function regression method comes not only from the functional nature of warping functions but also from their inherent constraints. To this end, a functional transformation is employed to transform warping functions into a vector space, and the confidence estimation for the regression operator is conducted in the vector space based on functional principal component analysis and bootstrapping. Then, the confidence region of the conditional expectation of missing distribution (caused by data loss) can be further estimated and visualized. In addition, a calibration processing procedure is also considered to obtain improved estimates of the confidence intervals with a better coverage accuracy under the desired probability. Simulation studies are conducted to validate and illustrate the proposed method, and then, it is applied to field strain monitoring data.

Vibration-based monitoring of a small-scale wind turbine blade under varying climate conditions. Part I: An experimental benchmark.

SummaryStructural health monitoring (SHM) has been increasingly exploited in recent years as a valuable tool for assessing performance throughout the life cycle of structural systems, as well as for supporting decision‐making and maintenance planning. Although a great assortment of SHM methods has been developed, only a limited number of studies exist serving as reference basis for the comparison of different techniques. In this paper, the vibration‐based assessment of a small‐scale wind turbine (WT) blade is experimentally investigated, with the aim of establishing a benchmark case study for the SHM community. The structure under consideration, provided by Sonkyo Energy as part of the Windspot 3.5 kW WT model, is tested in both healthy and damaged states under varying environmental, that is, temperature, conditions as imposed by means of a climatic chamber. This study offers a thorough documentation of the configuration of this experimental benchmark, including the types of deployed sensors, the nature of excitation and available measurements, and the investigated damage scenarios and environmental variations enforced. Lastly, an overview of the raw and processed measurement data, made available to researchers via an open access Zenodo repository, is herein provided.

Domain Adaptation for Structural Health Monitoring

In recent years, machine learning (ML) algorithms gained a lot of interest within structural health monitoring (SHM) community. Many of those approaches assume the training and test data come from similar distributions. However, real-world applications, where an ML model is trained on numerical simulation data and tested on experimental data, are deemed to fail in detecting the damage, as both domain data are collected under different conditions and they don’t share the same underlying features. This paper proposes the domain adaptation approach as a solution to particular SHM problems where the classifier has access to the labeled training (source) and unlabeled test (target) domains. The proposed domain adaptation method forms a feature space to match the latent features of both source and target domains. To evaluate the performance of this approach, we present a case study where we train three neural network-based classifiers on a three-story test structure: i) Classifier A uses labeled simulation data from the numerical model of the test structure; ii) Classifier B utilizes labeled experimental data from the test structure; and iii) Classifier C implements domain adaptation by training on labeled simulation data (source) and unlabeled experimental data (target). The performance of each classifier is evaluated by computing the accuracy of the discrimination against labeled experimental data. Overall, the results demonstrate that domain adaption can be regarded as a valid approach for SHM applications where access to labeled experimental data is limited.

Electromechanical Impedance Model for Free 1D Thin-Walled Piezoelectric Ceramics with a Novel Derivation.

The electromechanical impedance model of the piezoelectric ceramics in a free state can be used for screening and quality control in the structural health monitoring community, but the derivation process of the existing model is usually complicated. This paper describes a novel theoretical derivation methodology based on the assumption of zero-stress on the free boundary of the one-dimensional transducer, which can simplify the derivation of the model to a large extent. To assess the accuracy of the model, a signal processing method based on frequency shifting transformation and the Pearson correlation coefficient is also proposed to calculate the similarity between theoretically predicted and experimentally measured data. Two different piezoelectric ceramics were used in experiments to verify the effectiveness of the model. Experimental results convincingly demonstrate that the assumption proposed in this paper possesses good feasibility for one-dimensional thin-walled piezoelectric ceramics and the model has excellent precision.

Detecting structural damage under unknown seismic excitation by deep convolutional neural network with wavelet-based transmissibility data

Many research groups in the structural health monitoring community have made efforts to utilize deep learning-based approaches for damage detection on a variety of structures. Among these approaches, structural damage detection through deep convolutional neural networks using raw structural response data has received great attention. However, structural responses are affected not only by structural properties but also by excitation characteristics. For detecting of structures’ damage under seismic excitations, different seismic excitations definitely cause varied structural responses data. In practice, it is impossible to accurately predict the characteristics of future seismic excitation for pre-training the deep convolutional neural network. Therefore, it is essential to investigate the autonomous detection of structural element damage subject to unknown seismic excitation. In this article, a new approach is proposed for detecting structural damage subject to unknown seismic excitation based on a convolutional neural network with wavelet-based transmissibility of structural response data. The transmissibility functions of structural response data are used to eliminate the influence of different seismic excitations. Moreover, contrary to the traditional Fourier transform in the conventional transmissibility function, wavelet-based transmissibility function is presented using the ability in subtle information acquisition of wavelet transform. The wavelet-based transmissibility data of structural responses are used as the inputs to constructed deep convolutional neural networks. Both a numerical simulation example and an experimental test are used to validate the performance of the proposed approach based on deep convolutional neural network.

Response‐based time‐invariant methods for damage localization on a concrete bridge

AbstractThis paper is integral part of the Special Issue on “Existing Concrete Structures: Structural Health Monitoring and Testing for condition assessment.” It deals with vibration‐based methods (VBMs) for damage localization that approach the problem of structural integrity management through the analysis of the dynamic response of the structure under ambient or forced vibrations. In the last years, these methods received a widespread interest in the structural health monitoring (SHM) community due to the possibility to use them for continuous SHM and real time damage identification. The performance of these methods is commonly verified on numerical models or laboratory specimens that, by their nature, cannot reproduce all the sources of uncertainties found in practice. The availability of data recorded on a real benchmark, the S101 bridge in Austria, enabled the comparison of three well known vibration‐based time‐invariant methods for damage localization, namely, the curvature method, the interpolation error method, and the strain energy method. The bridge, built in the early 1960, is a typical example of a European highway bridge. Responses to ambient vibrations were recorded both in the undamaged and in several different damage scenarios artificially inflicted to the bridge. This paper reports the results of the application of the three mentioned methods of damage localization to this case study.

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

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.