Vibration-based Damage Detection Research Articles

Vibration-based damage detection method with tunable resolution for composite laminates

Composite laminates are increasingly applied in advanced engineering structures, but they are prone to be damaged in service. However, multiple defects with different sizes challenge the current damage detection methods to identify all of them without a baseline. In this paper, a baseline-free damage identification method based on vibration is developed, and a novel damage index with tunable resolution of detection is proposed based on two-dimensional continuous wavelet transform by manipulating wavelet parameters. The relationship between the smallest detectable damage size and wavelet parameters is explored quantitatively, and the effectivity and reliability of proposed method are numerically and experimentally verified to directly detect damage in composite laminates without baselines. By a comparison with other vibration-based damage detection methods, results show that the proposed method can precisely locate and visualize multi-damage with determinate resolution of detection, and can realize multi-resolution of damage detection for identifying various sizes of defects.

Application of variational mode decomposition in vibration-based bridge damage detection

Bridges are critical infrastructure that require continuous structural health monitoring to ensure safety. Vibration-based methods use changes in dynamic response for damage detection. In this study, variational mode decomposition (VMD), a signal analysis technique, was applied to detect damage in a three-span beam bridge model subjected to moving loads. A finite-element model was developed to represent the bridge–vehicle interaction and damage was simulated by reducing the flexural stiffness of the bridge. Sensors placed on each span recorded the vibrations during vehicle passage. VMD was used to decompose the signals for analysis of damage indications. Different damage levels (30–50% stiffness reduction) and locations (each span) were evaluated. The vehicle speed was also varied to assess its effect. Damage was identified as peaks in the instantaneous energy diagrams of decomposed signals, correctly indicating the axle locations. Detection was improved with sensors nearer the damage and lower vehicle speed. The results demonstrate the ability of VMD to detect and locate damage without a baseline, indicating its potential as a non-baseline vibration-based damage detection method for bridges.

Image-based machine learning approach for structural damage detection through wavelet transforms

Structural integrity is essential for safety in infrastructure, as it can help prevent catastrophic failures and financial losses. The significance of vibration-based damage detection has grown substantially in fields such as civil and mechanical engineering. Concurrently, the advancements in computational capacities have facilitated the integration of machine learning into damage detection processes through post-processing algorithms. Nevertheless, these require extensive data from structure-affixed sensors, raising computational requirements. In an effort to address this challenge, we propose a novel approach utilizing a pre-trained convolutional neural network (CNN) based on images to identify and assess structural damage. This method involves employing wavelet transform and scalograms to convert numerical acceleration data into image data, preserving spatial and temporal information more effectively compared to conventional Fourier transform frequency analysis. Six acceleration data channels are collected from carefully chosen nodes on a mini bridge model and a corresponding finite element bridge model, to train the CNN. The efficiency of training is further enhanced by applying transfer machine learning through two pre-trained CNNs, namely Alexnet and Resnet. We evaluate our method using different damage scenarios, and both Alexnet and Resnet show prediction accuracies over 90%.

Multi-task Framework for Vibration-Based Structural Damage Detection of Spatial Truss Structure Using Graph Learning

Multi-task Framework for Vibration-Based Structural Damage Detection of Spatial Truss Structure Using Graph Learning

An experimental study on dynamic response of cement concrete pavement under vehicle load using IoT MEMS acceleration sensor network

Understanding the dynamic response of pavements is crucial for comprehending pavement deformation and interaction mechanisms under vehicle load. There is a notable absence of monitoring systems that involve the in-situ embedding of acceleration sensors within full-scale cement concrete pavement slabs. This research introduces an IoT system designed for the monitoring of pavement vibration responses, with its deployment and installation executed within a field engineering project. By collecting and analyzing the pavement vibration responses to vehicle loads, the study investigates the time–frequency characteristics of vibration signals across various vehicle speeds, sensor depths, and lateral distances. The findings reveal that both the amplitude and main frequency of the vibration signal escalate with vehicle speed, whereas the embedding depth of the sensor impacts only the amplitude, leaving the main frequency unaffected. Through statistical analysis, it was determined that effective sensor spacing on cement concrete pavements should not exceed 120 cm to avoid significant signal attenuation, which on average reaches 50 % at a sensor spacing of 90 cm. These insights offer critical guidance for optimizing sensor layouts on cement concrete pavements and lay the foundation for future research on vibration-based damage detection and vehicle information analysis.

Comparative Analysis of Structural Damage Identification Methods Based on Iterative Reweighted L1/2 Regularization and Three Optimization Functions

Previous vibration-based damage detection studies mostly focus on developing a more sensitive optimization function to promote the effectiveness of damage identification. However, a few studies have conducted comparative analyses on the detection performance of different optimization functions. In the study, changes in the frequency and mode shape are applied as the inputs to different optimization functions for damage identification. Three optimization functions are established using the frequency residuals, the combinations of frequency and mode shape residuals, and the modal flexibility residuals, respectively. Considering the sparsity of damage element distribution, an iterative reweighted [Formula: see text] [Formula: see text] regularization is added as a norm penalty to the optimization function. A numerical model and an experimental example are applied to assess the performance of distinct optimization functions. The results show that the increase in modal data number cannot significantly improve the detection accuracy when the number meets the basic requirements for identifying damage. The detection error of the optimization function established by combining the frequency and mode shape residuals is 6.65% and 5.18% using the first four and fourteen-order noisy modal data, respectively. Furthermore, the optimization function constructed using the modal flexibility residuals requires more less modal data to identify damage than the other two functions.

Towards vibration-based damage detection of civil engineering structures: overview, challenges, and future prospects

AbstractIn this paper, we delve into the evolving landscape of vibration-based structural damage detection (SDD) methodologies, emphasizing the pivotal role civil structures play in society's wellbeing and progress. While the significance of monitoring the resilience, durability, and overall health of these structures remains paramount, the methodology employed is continually evolving. Our focus encompasses not just the transformation brought by the advent of artificial intelligence but also the nuanced challenges and future directions that emerge from this integration. We shed light on the inherent nonlinearities civil engineering structures face, the limitations of current validation metrics, and the conundrums introduced by inverse analysis. Highlighting machine learning's (ML) transformative role, we discuss how techniques such as artificial neural networks and support vector machine's have expanded the SDD's scope. Deep learning's (DL) contributions, especially the innovative capabilities of convolutional neural network in raw data feature extraction, are elaborated upon, juxtaposed with the potential pitfalls, like data overfitting. We propose future avenues for the field, such as blending undamaged real-world data with simulated damage scenarios and a tilt towards unsupervised algorithms. By synthesizing these insights, our review offers an updated perspective on the amalgamation of traditional SDD techniques with ML and DL, underlining their potential in fostering more robust civil infrastructures.

Vibration based damage detection for hybrid composite cantilever beam using finite element analysis and artificial neural network

Damage in mechanical structure causes a change in its physical properties and it will affect the real-time application. Cracks in a structure that distressed in model parameters like mode shape, and natural frequency. Its need to the identification of damage early to avoid catastrophic failure and increase the life of the mechanical structure. In this paper, a model for natural fundamental vibration analysis of cantilever beam with the inclined crack with different depths and different locations has been presented. The natural frequency of crack reduces by an increase in crack depth. The performances of results have been verified with finite element analyses software ANSYS, Experimental analysis and finally compare results with a neural network.

Experimental and Numerical Study on Vibration-Based Damage Detection and Localization in Laminated Composite Plates

Damage detection in composite materials is crucial for ensuring the safety and reliability of engineering structures. Conventional methods often face challenges in accurately identifying damage in plate-like structures, particularly in scenarios involving multiple damages or small-scale delamination. This study focuses on investigating the detection and localization of delamination in composite plates by employing both experimental and numerical modal analysis. An eight-ply woven Glass-Epoxy composite laminate with and without damage was prepared with the aid of hand lamination technique. Laminate was fixed to a Clamped-Free-Free-Free (CFFF) boundary condition for experimental modal analysis by introducing controlled damage to examine its impact on modal properties. To validate the natural frequencies (NFs) of damaged and undamaged composite laminates, a numerical analysis was conducted using ANSYS Parametric Design Language (APDL). Further, to advance the understanding of using modal shapes and their spatial derivatives for damage localization in composite plates under various damage situations, post-processing of simulation results was conducted using MATLAB. Finite Difference Method has been employed to calculate the derivatives, and a novel damage index (DI) is proposed to enhance damage localization capabilities. The results affirm that the proposed DI is effective and precise in identifying damage in plate-like structures, both for individual and multiple damage scenarios. This research study presents a novel approach for identifying and pinpointing damage in composite plates, thereby making a valuable contribution to the field of structural health monitoring (SHM) applications

Vibration-based structural damage detection strategy using FRFs and machine learning classifiers

In this paper, a damage detection strategy for beam-type structures based on frequency response functions (FRFs) is presented. Five aluminum beams of equal nominal dimensions are used in an experimental program under laboratory conditions to obtain experimental FRF data from both undamaged and damaged conditions. Damages are induced by creating rectangular notches on the beams using saw-cutting. A stochastic finite element model of the beam is developed in MATLAB to construct several training datasets. The damages are modeled by reducing the cross-sectional area at the corresponding damaged elements. Simple damage indexes are proposed as damage-sensitive features. Decision Tree, Support Vector Machines, and Artificial Neural Networks classifiers are trained in the first stage to perform damage detection and localization. Single and multiple damages located in a single zone and more than one zone simultaneously are considered. In the second stage, experimental data not used for training are used for validation. The results from the first stage suggest that the proposed damage indexes can effectively detect and locate structural damage in beams. Among all classifiers, Artificial Neural Networks is the classifier that best performed. High accuracy is achieved to identify the presence of damage (99.3%) and detecting its location on the beam for some of the training datasets (from 80.0% to 97.1%). In the second stage of validation, accuracy, as expected, decreased. However, misclassifications occur mainly for FRF samples in the impact zone, which indicates that the proposed strategy can be efficient to detect damages at locations other than the excitation zone.

VIBRATION-BASED DAMAGE DETECTION FOR ONE-STORY STEEL FRAME STRUCTURE USING MODE SHAPE CURVATURE

Structural health monitoring techniques, particularly vibration-based damage detection, have gained significance in assessing civil structure condition. This paper focuses on utilising mode shape curvature for damage detection in a one-story steel frame structure. The study aims to overcome traditional inspection limitations by exploring vibration-based approaches. Experimental investigation is conducted to analyse intact and damaged structural modal behaviour. Modal analysis technique extracts modal frequencies and mode shapes, enabling analysis of mode shape curvature for damage detection and localisation. Preliminary findings show that damaged structures display deviations in mode shapes and reduced natural frequencies, providing evidence of structural damage. However, a significant issue arises near the support, where unexpected patterns emerge in the Total Damage Index (TDI) with increasing damage severity. This finding challenges the expected correlation between severity levels and TDI values, highlighting the need to consider factors like fixed supports. Misleading signs of damage in some segments underscore the importance of cautious result interpretation and accounting for noise. Future studies should focus on noise resistance, false indication mitigation, and understanding segments with fixed supports to enhance mode shape curvature analysis’s reliability for damage detection in civil structures.

An FDD-based modal parameter-less proportional flexibility-resembling matrix for response-only damage detection

Modal flexibility-based methods are effective tools for vibration-based structural damage detection, including in the output-only case. These methods are typically characterized by two stages: first, the modal parameters are identified, thus obtaining a certain number of modes; second, these modal parameters are used to assemble the modal flexibility matrix. This paper proposes a method for estimating a matrix that approximates a proportional flexibility matrix, termed proportional flexibility-resembling (PFR) matrix, and shows that this matrix can be used for damage detection and localization purposes. This matrix is obtained through signal processing operations to be executed after applying the first steps of the frequency-domain decomposition (FDD) technique—i.e., after the singular value decomposition of the spectral density matrix. The defining aspect of the PFR matrix is that, differently from the traditional formulation of modal flexibility and proportional flexibility matrices, it can be assembled without the need of an explicit identification of the modal parameters. In fact, the matrix is estimated by processing all first singular vectors and also all first singular values in a selected frequency range. In the proposed method, the typical two stage approach of traditional modal flexibility methods is avoided, and the intervention of an operator is limited to setting the values of a few parameters in the initial phase of the process. Numerical simulations and experimental data from a testbed structure were used to show the effectiveness of the proposed approach, and the analyses were performed by considering structures with different damage scenarios and damping properties.

A Review on Vibration-Based Damage Detection Methods for Civil Structures

Vibration-based damage detection is a range of methods that utilizes the dynamic response of a structure to evaluate its condition and detect damage. It is an important approach for structural health monitoring and has drawn much attention from researchers. While multiple reviews have been published focusing on different aspects of this field, there has not been a study specifically examining the recent development across the range of methods, including natural frequency, mode shape, modal curvature, modal strain energy, and modal flexibility-based damage detection methods. This paper aims to fill this gap by reviewing the recent application of these methods in civil structures, including beams, plates, trusses, frames, and composite structural members. The merits and limitations of each method are discussed, and research opportunities are presented. This broader review also provides an opportunity for critical comparison across this range of methods. While predominantly reviewing experiment-based studies, this review also considers some numerical studies that may motivate further research.

Detection of Structural Damage in a Shaking Table Test Based on an Auto-Regressive Model with Additive Noise

Damage identification plays an important role in enhancing resilience by facilitating precise detection and assessment of structural impairments, thereby strengthening the resilience of critical infrastructure. A current challenge of vibration-based damage detection methods is the difficulty of enhancing the precision of the detection results. This problem can be approached through improving the noise reduction performance of algorithms. A novel method based partially on the errors-in-variables (EIV) model and its total least-squares (LS) algorithm is proposed in this study. Compared with a classical damage detection approach involving adoption of auto-regressive (AR) models and the least-squares (LS) method, the proposed method accounts for all the observation errors as well as the relationships between them, especially in an elevated level of noise, which leads to a better accuracy. Accordingly, a shaking table test and its corresponding finite element simulation of a full-scale web steel structure were conducted. The acceleration time-series output data of the model after suffering from different seismic intensities were used to identify damage using the presented detection method. The response and identification results of the experiment and the finite element analysis are consistent. The finding of this paper indicated that the presented approach is capable of detecting damage with a higher accuracy, especially when the signal noise is high.

Vibration based damage detection of steel plate

It is very important to monitor the occurrence of damage of various structures. By early detection of damage and by taking the proper corrective measures the failure of damage could be avoided. When damage occurs in structure it results in change in stiffness properties of the structure which in turn result in change in modal properties like natural frequency, mode shape change, mode shape curvature and strain energy of a structure. These parameters are used as an input to train the artificial neural network(ANN). The efficiency of the ANN to predict the damage at single and multiple damage cases using the change in natural frequency and strain energy was evaluated in this study. The efficiency of individual modes and combination of frequency and strain energy to detect damage is also considered in this study. The modal parameters were obtained by carrying out modal analysis using ANSYS 2021 R1 software and the ANN was developed using MATLAB software. The results indicated that the strain energy accurately predict the damage location and varies linearly with severity of damage. It is better to use combination of frequency and strain energy as indicator to detect damages.

Vibration-based hidden damage imaging using stereo cameras with digital image correlation

This paper explores a full-field non-contact optical sensing technique using a stereo camera for imaging hidden damage based on vibration-based damage detection methodology in structural health monitoring. The technique utilizes a pair of digital cameras to capture dynamic operational deflection shapes (ODSs) over the region of interest (ROI) of a structure’s surface via digital image correlation (DIC) when subjected to vibrational excitation. This research overcomes bottlenecks in using high vibration modes for imaging the hidden damage area by (1) applying DIC to operational modal analysis with simple pick-peaking techniques to gather natural frequencies and operational mode shapes in plate structures, while (2) using wavelet analysis to reveal the image of the damage region as a means for baseline-free global damage quantification. In the feasibility study, four cases with two aluminum plates with large damage regions were investigated with a vibration shaker generating a frequency sweep up to 1 kHz. The stereo camera imaged the speckled surface of the plate with white light. Once the dynamic ODSs in the ROI were observed using DIC, the natural frequencies and associated operational mode shapes were extracted using a peak-picking technique in the frequency spectrum. Natural frequencies and operational mode shapes from finite element analysis correlated well with the experimental observations from three-dimensional DIC for all 12 vibration modes respectively. A wavelet transform mode shape curvature (WT-MSC) technique to obtain the modal shape curvature via a two-dimensional continuous wavelet transform with a Mexican Hat analyzing wavelet was then implemented on each of the first 12 vibration mode shapes. A damage image condition that incorporates all weighted wavelet coefficients is proposed to image the damage region. The hidden damage was visualized clearly with WT-MSC, as the technique is much less sensitive to noise than the use of MSC alone, and the use of high vibration modes exhibiting larger mode shape curvatures provided a greater sensitivity for imaging the damage region. Hidden damage regions were successfully visualized in all four cases.

Assessment of background noise properties in time and time–frequency domains in the context of vibration-based local damage detection in real environment

Any measurement in condition monitoring applications is associated with disturbing noise. Till now, most of the diagnostic procedures have assumed the Gaussian distribution for the noise. This paper shares a novel perspective to the problem of local damage detection. The acquired vector of observations is considered as an additive mixture of signal of interest (SOI) and noise with strongly non-Gaussian, heavy-tailed properties, that masks the SOI. The distribution properties of the background noise influence the selection of tools used for the signal analysis, particularly for local damage detection. Thus, it is extremely important to recognize and identify possible non-Gaussian behavior of the noise. The problem considered here is more general than the classical goodness-of-fit testing. The paper highlights the important role of variance, as most of the methods for signal analysis are based on the assumption of the finite-variance distribution of the underlying signal. The finite variance assumption is crucial but implicit to most indicators used in condition monitoring (such as the root-mean-square value, the power spectral density, the kurtosis, the spectral correlation, etc.), in view that infinite variance implies moments higher than 2 are also infinite. The problem is demonstrated based on three popular types of non-Gaussian distributions observed for real vibration signals. We demonstrate how the properties of noise distribution in the time domain may change by its transformations to the time–frequency domain (spectrogram). Additionally, we propose a procedure to check the presence of the infinite-variance of the background noise. Our investigations are illustrated using simulation studies and real vibration signals from various machines.

Identification of fault frequency variation in the envelope spectrum in the vibration-based local damage detection in possible changing load/speed conditions

The problem of local damage diagnosis (based on the detection of impulsive and periodic signals) is discussed. Both features should be checked, as fault frequency must be linked to the true value calculated for a given machine and speed. The precise estimation of the fault frequency is hard due to several factors. If a speed fluctuation exists, it is solved by order analysis. A wider perspective is proposed here, namely, an automatic statistical approach to analyze the distribution of estimated fault frequencies. We propose a procedure to evaluate whether the fault frequency is constant or not. The algorithm uses frequency estimation based on peak detection in the envelope spectrum and statistical testing. We present simulation studies and industrial examples. We have found that if the fault frequency is not constant and its distribution does not follow Gaussian shape with minor variance, then one should use more advanced techniques, e.g. order analysis.

Damage Detection of Structures Considering Environmental Variations Based on the Deviation Patterns of Natural Frequencies

The fundamental objective of structural damage detection is to ascertain the presence of damage in the structure under monitoring. Environmental variations (especially temperature) may also cause the aberration of dynamic properties. This could result in false alarms and pose a threat to the reliability of vibration-based damage detection. In this paper, a new approach toward damage detection under varying environmental conditions is investigated based on the deviation patterns of natural frequencies. The idea is derived from the fact that most damage occurs in a local area of a structure while environmental variations affect the whole structure. Natural frequencies deviate from their previous state in different patterns based on the causation of the deviation. The method uses simple mathematics to distinguish the two patterns using the first 10 natural frequencies of a structure. The proposed method was applied in two case studies including a numerical model with severe temperature variations simulated in ABAQUS and 1-year health monitoring data of a cable-stayed bridge in China as a real case study. By implementing the proposed method, the effects of environmental variations were eliminated in both case studies and damage was accurately detected before it was evident to the visual inspections of the bridge.

Non-negative tensor factorization for vibration-based local damage detection

In this study, a novel non-negative tensor factorization (NTF)-based method for vibration-based local damage detection in rolling element bearings is proposed. As the diagnostic signal registered from a faulty machine is non-stationary, the time–frequency method is frequently used as a primary decomposition technique. It is proposed here to extract multi-linear NTF-based components from a 3D array of time–frequency representations of an observed signal partitioned into blocks. As a result, frequency and temporal informative components can be efficiently separated from non-informative ones. The experiments performed on synthetic and real signals demonstrate the high efficiency of the proposed method with respect to the already known non-negative matrix factorization approach.