Damage Detection In Plates Research Articles

Enhancing Damage Detection in 2D Concrete Plates: A Comprehensive Study on Interpolation Methods and Parameters

In an era marked by increasing demands for stability and durability in construction, the importance of damage detection in concrete structures cannot be overstated. As these structures underpin the safety and longevity of vital assets, this paper embarks on a comprehensive exploration of methodologies to enhance precision and reliability in 2D concrete plate damage detection. By focusing on the interpolation of damage index values and leveraging the insights gained from energy loss analysis and the characterization of the time of arrival of signals, we address the pressing need for improved non-destructive damage detection techniques. Our study encompasses a range of simulation attempts, each involving various interpolation parameters, and systematically evaluates their performance. The culmination of this research identifies the most effective combination of techniques and parameters, leading to the best results in damage detection. This multidimensional investigation promises to provide valuable contributions to the field of structural health monitoring, benefiting both researchers and practitioners engaged in the evaluation of concrete structures.

Directional wavelet modal curvature method for damage detection in plates

Abstract Vibration-based damage detection using the 2D modal curvature has become a research focus. However, the traditional methods based on modal curvature lack the sensitivity to direction of the damage. To address this problem, a directional 2D Morlet wavelet modal curvature is proposed in this paper. The directional wavelet modal curvatures show the following merits: robust to noise interference and sensitive to direction of damage. By the numerical simulation, the efficiency of the proposed method is demonstrated in the plate-like structure.

Damage detection in plates based on Lamb wavefront shape reconstruction

Many of the current studies in the area of damage detection using elastic wave propagation are based on deploying sensor networks with a large number of piezoelectric transducers to detect small-size cracks. A major limitation of these studies is that cracks are usually larger and have different shapes in real cases. Moreover, using a large number of sensing nodes for damage detection is both costly and computationally intensive. The paper proposes a novel elastic wave propagation method for damage detection based on the reconstruction of the shape of the reflected wavefront. The main advantage of the developed approach is that merely three sensors are necessary to localize and estimate the size of the cracks with various sizes and shapes. Several damage cases with different number of piezoelectric transducers and placement have been investigated experimentally. Extensive numerical simulations are performed to benchmark the performance of the proposed method.

Damage Detection in Plates with the Use of Laser-Measured Mode Shapes

This paper presents the use of laser vibrometer measurements to detect and locate damage in a metal plate. An algorithm based on local spatial filters was selected, and for the purpose of comparison, the fault location was also determined based on the wavelet analysis of mode shapes. The research was carried out first on the created finite element model of aluminum plate, where two kinds of damage of increasing size and temperature change were simulated. After obtaining positive results, a laboratory experiment was carried out, which consisted of measuring the vibration of the aluminum plate with the laser vibrometer in undamaged condition, at increased temperatures, and with various damage scenarios. The conclusions of the laboratory experiment confirm the damage detection capabilities of the methods but question their damage localization potential.

Structural damage detection in plates using a deep neural network–couple sparse coding classification ensemble method

The present study aims at identifying damages in plate structures by applying a pattern recognition–based damage detection technique using the frequency response function. The large number of degrees of freedom is one of the crucial obstacles in the way of accurately identifying damages in plate structures. On the other hand, frequency response functions include many details that dramatically lower the computing speed and enlarge the memory needed for storing data, hampering the application of this method. Furthermore, this study performs principal component analysis as an authoritative feature extraction method with the purpose of reducing the dimensions of the measured frequency response function data and generating distinct feature patterns. Also, because there has been no individual optimal classifier applicable to all problems, an ensemble comprising two powerful classifiers containing couple sparse coding classification and deep neural networks is used to predict the structure damage. This study evaluates the accuracy of damage detection by the proposed method in square-shaped structural plates with the lengths of 1 m and 2 m under different damage scenarios, namely, single and multiple element.

Nonlinear guided wave technique for localized damage detection in plates with surface-bonded sensors to receive Lamb waves generated by shear-horizontal wave mixing

Mutual wave interactions provide a very promising technique for nondestructive testing and evaluation due to their exceptional sensitivity to micro-scale damage growth in metallic materials. This article describes detection of localized fatigue damage in aluminum plates using polyvinylidene difluorine (PVDF) sensors to simultaneously receive shear-horizontal waves and the secondary Lamb waves that they generate. Finite element simulations explore several aspects of wave interaction and mode identification. Then laboratory experiments confirm secondary wave generation at the sum frequency in an aluminum plate using the PVDF sensor by conducting frequency-wave number domain and supporting analyses. The PVDF sensor enables computation of amplitude ratios even though the primary and secondary waves have different polarities. Finally, a simple guided wave technique based on electrically scanning the wave mixing zone around a plate is shown to detect early localized fatigue damage in an aluminum plate.

Sparsity-based approaches for damage detection in plates

Abstract The data deluge in Structural Health Monitoring (SHM) and the need for automated online damage detection systems necessitates a move away from traditional model-based approaches. To that end, we propose sparsity-based algorithms for damage detection in plates. Instead of high-fidelity models, our proposed algorithms use dictionaries, consisting of response signals acquired directly from the system of interest, as the key feature to both detect and localize damages. We address the damage detection problem both when the damage is located on or off a grid of possible damage coordinates defined by the dictionary. This gives rise to two classes of problems, namely, on the grid and off the grid problems. In our sparsity-based on the grid damage detection (SDD-ON) platform, we solve a LASSO regression problem, where, the unknown vector is a pointer for existence of damage at the various locations defined on the grid used for dictionary construction. In our proposed off the grid damage detection (SDD-OFF) platform, we use a penalized regression algorithm to extend the dictionary of measured damage signals to points off-the-grid by linear interpolation. We evaluate the performance of both SDD frameworks, in detecting damages on plates, using finite element simulations as well as laboratory experiments involving a pitch-catch setup using a single actuator-sensor pair. Our results suggest that the proposed algorithms perform damage detection in plates efficiently. We obtain area under receiver operating characteristic (ROC) curves of 0.997 and 0.8314 for SDD-ON and SDD-OFF, respectively.

Selected problems of damage detection in internally supported plates using one-dimensional Discrete Wavelet Transform

The paper is concerned with damage detection in plates while using the Discrete Wavelet Transform (DWT). Rectangular plate structures resting on a Winkler or elastic half-space type foundation, with free boundaries are examined. Plate bending is described and solved by the Boundary Element Method in a direct approach. Defects are introduced by additional edges forming slots in relation to the basic plate domain. Numerical investigation is conducted basing on signal analysis of the structural static response and by taking advantage of multi-resolution analysis (MRA) of the signal function which can be represented in a multi-scale manner. The obtained signal is decomposed with the use of Daubechies or Coiflet wavelet families. The white noise generator is used to model measurement inaccuracy which is an inevitable element of a real experiment. The efficiency of DWT of the contaminated signal in damage detection is studied.

Probabilistic Diagnostic Algorithm-Based Damage Detection for Plates with Non-uniform Sections Using the Improved Weight Function

The current studies on damage detection based on guided wave mainly focus on uniform-section plate-like structures. However, plates with non-uniform sections are also essential structures in some engineering applications, such as ships, bridges, aircraft, and buildings. It is essential to identify damage in the non-uniform-section plates (NUSPs). An improvement is put forward to make reconstruction algorithm for the probability inspection of damage (RAPID) feasible on NUSPs. Reconstruction algorithm for the probability inspection of damage (RAPID) is a promising kind of tomography technique for detection and supervising of structures. The damage signatures based on the differences between the Lamb wave signals captured from the undamaged (reference) and damaged (present) states are used to assess the probability of the damage. The elliptical weight function commonly used in the RAPID algorithm limits the applications of the method in identifying damage in non-uniform-section plates. In this study, a novel method is proposed to choose the weight function that satisfies not only uniform-section plates but also NUSPs. A series of experiments on aluminum plates are carried out to validate the correctness of the method mentioned before. The results indicate that the improvement considerably increases the precision of the localization of damage in NUSPs.

Omnidirectional shear horizontal wave based tomography for damage detection in a metallic plate with the compensation for the transfer functions of transducer

Guided-wave based damage detection for plates has been widely studied for structural health monitoring. Because most of the earlier studies used dispersive Lamb waves, substantial efforts had to be made to alleviate the dispersive and multi-modal nature of Lamb waves and the effect of surface conditions. In contrast, shear-horizontal (SH) waves have better propagation characteristics suitable for the detection of damages in plates, but SH waves have not been widely used due to the lack of efficient methods to generate and sense omnidirectional SH waves. The objective of this study is to construct diagnostic images of damaged plates by using omnidirectional SH waves with a special emphasis on the compensation of the frequency-dependence of the SH wave transducers. The compensation is necessary to have reliable diagnostic images because its frequency-dependent characteristics considerably can affect imaging quality if they are not considered. Consequently, simplified, yet effective, models representing the transfer functions of the omnidirectional SH wave magnetostrictive patch transducer (OSH-MPT) are developed in this paper. To visualize the position and shape of the structural damages in a metallic plate, the virtual time-reversal imaging method is used and two alternative techniques are considered to compensate for the effects of the transfer functions of transducers in the imaging processes. The imaging results after the compensation appear quite promising, suggesting that the omnidirectional SH wave based enhanced time-reversal imaging can be an efficient inspection method for plate structures.

Cooperative Coevolution with Dynamic Species-Size Strategy for Vibration-Based Damage Detection in Plates

Vibration-based damage detection is based on the fact that vibration characteristics such as natural frequencies and mode shapes of structures are changed when the damage occurs. This paper proposes dynamic species-size strategy in cooperative coevolution concept. The resulting algorithm, cooperative coevolutionary genetic algorithm with dynamics species-size (CCGADSS), is used as the optimization algorithm in the vibration-based damage detection in plates. The objective function is numerically calculated from the difference between experimentally vibration characteristics and numerically evaluated vibration characteristics of the predicted damage. In finite element model for objective calculation, the plates are equally divided into 64 elements. There are 2 different cases with dissimilar occurred damage in plates are considered. In first case, the plate hase only one region consisting of 4 elements which are together connected and have same damage. In second case, there are 5 separated elements which are damaged differently. In order to demonstrate the performance of the dynamic species-size strategy, 3 optimization algorithms, which are genetic algorithm (GA), cooperative coevolutionary genetic algorithm (CCGA), and CCGADSS. The results indicate that CCGADSS is superior to GA and CCGA. Moreover solutions obtained using CCGADSS are quite close the actual damage. These results show that the dynamic species-size strategy can enhance performance of cooperative coevolution concept.

Damage detection in plates using the electromechanical impedance technique based on decoupled measurements of piezoelectric transducers

Electromechanical impedance (EMI) technique plays an important role in the monitoring of structures with piezoelectric transducers (PT). According to the EMI technique, diagnosis and prognosis can be carried out to detect structural modifications in an operative state. However, to develop an efficient methodology for damage detection; damage metrics and patterns should be defined using indices to quantify changes in the signals. In this study, a new approach is proposed considering the electrical impedance (EI) of PT as an array of coupled electrical impedances. It means that when a PT is bonded to a structure, the EI is governed by an electrical circuit that is assumed to be parallel and composed by electrical contributions of both, the structure and the PT. In our perspective, each free PT presents unique mechanical characteristics and those differences may influence the measured electrical signals, therefore the electrical contributions generated by each piezo-transducer are taken into account. To evaluate the electrical decoupling, two methodologies of damage detection are proposed to identify and locate an induced damage. In these methodologies, the damage metrics are based on ellipses of Gaussian confidence. Four experimental tests were performed to evaluate the methodologies, applying two damage intensities. The results show that the partial process of identification of a damage type is a feasible and applicable procedure, moreover the proposed method was able to evidence the damage location..

Scattering of the fundamental anti-symmetric Lamb wave at through-thickness notches in isotropic plates

Lamb wave based structural health monitoring technique is one of the promising techniques for detecting damage in structures. Among different types of guided waves, the fundamental anti-symmetric mode of Lamb wave (A0) has attracted considerable attention for damage detection in plates due to its outstanding properties, such as sensitivity to small and different types of defects, which make it a powerful tool for damage detection. Cracks are one of the major culprits in metallic structures which affect their durability and serviceability. Once the crack appears on the structures, it can affect its serviceability. It is important to detect cracks at early stage to avoid any catastrophic failure of the structures. This paper presents a study on the capability of using the A0 Lamb wave in detecting crack-like defects in aluminium plates, in which the scattering behaviour of the A0 Lamb wave at through-thickness notches in aluminium plates is studied in detail. 3D explicit finite element simulations are used to provide a comprehensive study on the scattering characteristics of the A0 Lamb wave at notches with different sizes and orientations. This study also considers different incident and scattered wave directions on the scattering characteristics. The results show that scattering characteristics of the A0 Lamb wave are sensitive to the size and orientation of notches. The findings of this study can improve the understanding of the capability of the A0 Lamb wave in detecting the crack-like defects and further advance damage detection techniques.

Two-dimensional curvature mode shape method based on wavelets and Teager energy for damage detection in plates

Vibration-based damage detection in plates has been investigated by various methods relying on mode shapes, among which the 2D curvature mode shape is a damage feature attracting much attention of researchers. Unlike the sound understanding of the use of the 1D curvature mode shape for detecting damage in beams, however, use of the 2D curvature mode shape to detect damage in plates is not yet well elucidated, major unresolved issues including lack of clarity about the mechanism of characterizing damage, susceptibility to noise, and insensitivity to sight damage. These deficiencies severely hamper use of the 2D curvature mode shape to portray damage in plates. To deal with these deficiencies, the mechanism of using 2D curvature mode shape to depict damage is analytically clarified in light of thin plate theory. On the basis of this clarification, a synergy between wavelet transform and a Teager energy operator is proposed to tackle the other deficiencies of susceptibility to noise and insensitivity to sight damage, leading an enhanced 2D curvature mode shape. The efficacy of the enhanced 2D curvature mode shape is numerically demonstrated using finite element simulations and experimentally validated through noncontact measurement by a scanning laser vibrometer, whereby its advances of clear mechanism of characterizing damage, robustness against noise, and sensitivity to slight damage are sufficiently corroborated.

Defect sensitivity of piezoelectric strip transducers based on planar Lamb waves

Long narrow piezoelectric fiber composite transducers that can generate planar ultrasonic guided waves are investigated for damage detection in plates. These strip transducers function well as actuators because the ultrasonic energy does not spread out and the signal remains strong. However, their suitability as sensors must be investigated because the received signal is integrated over the entire length of the transducer, potentially making them less sensitive to localized damage since the voltage is averaged over the length of the sensor. A segmented sensing scheme is introduced here and studied both numerically and experimentally. The strip transducer is subdivided into n segments, where n varies from 1 to 11, and the sensitivity to a through-thickness slot is assessed. The results indicate that there is a considerable improvement in defect sensitivity provided by segmented sensing. For a 130-mm-long strip transducer and a 15-mm-long slot, the transmission coefficient decreased from 0.86 to 0.70 to 0.59, while the reflection factor increased from 0.05 to 0.30 to 0.36 for the full 130-mm strip, a 31-mm-long segment, and a 7-mm-long segment, respectively.

Damage detection in plates using two-dimensional directional Gaussian wavelets and laser scanned operating deflection shapes

Mode shape analysis by wavelet transform has been used effectively for vibration-based damage detection in plates. As an extension of previous studies, this study focuses on an improved method for damage detection in plates: scrutiny of operating deflection shapes by two-dimensional directional Gaussian wavelet transforms. With this method, the proposed two-dimensional directional Gaussian wavelet can characterize directional information about damage; moreover, the operating deflection shapes can be used to address the real-time dynamic characteristics of a plate. To identify damage, the local surface of the plate is scanned using a scanning laser vibrometer to generate the local operating deflection shape, which is interrogated by two-dimensional directional Gaussian wavelets for damage. The feasibility of the method is numerically demonstrated using a low-magnitude operating deflection shape of a two-sided clamped plate, incorporating white noise with signal-to-noise ratio of 40 dB. The applicability of the method is then experimentally validated by detecting a cross-like notch in a suspended aluminum plate with the operating deflection shapes measured by a scanning laser vibrometer. Numerical and experimental results show that the method is capable of revealing directional features of small damage with high precision and strong robustness against noise. It appears that this damage detection method is related only to the spatially distributed measurement of vibrational responses in local critical regions of the plate. With this local property, the method requires no numerical or physical benchmark models for the entire structure in question nor any prior knowledge of either the material properties or the boundary conditions of the structure. (The Matlab code performing directional Gaussian wavelet transform can be provided by the corresponding author as per request.)

Crack location identification of rotating rotor systems using operating deflection shape data

Crack location identification, as one key destination of structural health monitoring, is still a challenge for operating rotor systems. The operating deflection shape (ODS), which represents a visual description of the structural vibration patterns under operating conditions, has been gaining importance for structure damage detection in recent years. The ODS carries damage information of a structure, however, it is also difficult to detect weak cracks of rotor directly. The approximate waveform capacity dimension (AWCD) method was successfully applied to damage detection of plates and beam-like structures. In this paper, a strategic approach that combines ODS and weighted AWCD is proposed for crack location identification of the rotating rotor. To eliminate the false peaks of AWCD and obtain desirable results, a weight factor and ODS curvature data are introduced to the expression of the weighted AWCD. The effectiveness of the proposed method is validated by numerical simulation and experimental investigation in a cracked rotor system. The results indicate that the proposed approach not only provides good identifying performance for incipient rotor cracks, but also effectively eliminates the fault peaks introduced by the inflexion locations of ODSs. Moreover, the proposed approach proves promising in detecting crack locations of rotating rotor systems.

Structural Steel Plate Damage Detection using Non Destructive Testing, Frame Energy based Statistical Features and Artificial Neural Networks

Abstract This paper discusses about the detection of damages present in the steel plates using nondestructive vibration testing. A simple experimental model has been developed to hold the steel plate complying with the simply supported boundary condition. Vibration patterns from the steel structure are captured based on the impact testing using a simple protocol. The vibration signals in normal condition of the steel plate are recorded. The damages of size 512 |x m to 1852 |j, m are simulated manually on the steel plate using drill bits. The vibration signals in the fault condition of the steel plate are collected. The captured vibration signals are preprocessed and time domain based feature extraction algorithms are developed to extract features from the vibration signals. The conditions of the steel plate namely healthy and faulty are associated with the extracted features to establish input output mapping. A feed-forward neural network is modeled to classify the condition. The neural network parameters are adjusted to train the network. The performance of the network is calculated using Falhman criterion.

Research on Plate Damage Detection by Two-Dimensional Wavelet Analysis

Wavelet analysis has the characteristics of timing and frequency adapting to each other. Two-dimensional wavelet analysis can rotate and amplify the signal in frequency domain. So, in this paper, two-dimensional wavelet analysis is used to detect the position of the injury of simply supported plate. This article simulates an injury and two injuries in a simply supported plate and the detection of the location of the injury. This article verifies that the feasibility of this method.

Damage detection in plates based on pattern search and Genetic algorithms

This paper is aimed at presenting two methods on the basis of pattern search and genetic algorithms to detect and estimate damage in plates using the modal data of a damaged plate. The proposed methods determine the damages of plate structures using optimization of an objective function by pattern search and genetic algorithms. These methods have been applied to two numerical examples, namely four-fixed supported and cantilever plates with and without noise in the modal data and containing one or several damages. The obtained results clearly reveal that the proposed methods can be viewed as a powerful and reliable method for structural damage detection in plates using the modal data.