Multiple Damage Identification Research Articles

A Comprehensive Study on the Selection of Mother Wavelets and Mode Shapes for Multiple Damage Identification

A Comprehensive Study on the Selection of Mother Wavelets and Mode Shapes for Multiple Damage Identification

A Baseline Free Method for Multiple Damage Identification and Localization using the Roving Mode Shape Response

The identification of structural systems with unknown vibration signature response is still a challenging issue which has been addressed by many reviewers. The current sensor technology states that the sensor position should be very close to the damaged element in order to identify and localize the damage. The primary goal of this research is to present a baseline-free method using the roving mode shape response based, multiple damage localization in a cantilever beam. Consequently, the damage location indicator is based on the roving mode shape approach (DLRA). The theoretical development is carried out on a cantilever beam, a finite element model. The different cases for multiple damages i.e. 2 elements damage, 3 elements damage and 5 elements to be damage, at a time, have been modelled on the structural member. The system response, for the healthy and damaged structural systems, has been determined using the roving mode shape approach. Further, the algorithm has been developed for multiple damage identification and localization using MATLAB software. The combined mass and stiffness damage, as well as only the mass change damage, both cases were considered. From the results, it was found that the proposed method can reliably identify the damage and its position. The method will also be helpful while keeping the sensor’s position very close to the damage. The novelty of this method is that it uses the response which is basically a field output and no prior assumptions have been made at the damaged element's location.

Multiple Damage Identification in a Beam Using Artificial Neural Network-Based Modified Mode Shape Curvature

Multiple Damage Identification in a Beam Using Artificial Neural Network-Based Modified Mode Shape Curvature

Improvement on modal curvatures methods for multiple damage identification in beams

PurposeThe purpose of this paper is to present a study on the application of four damage factors to several single and multiple damage scenarios of aluminium beams. Each one of these damage factors is defined by the information given by modal curvatures of the beams.Design/methodology/approachThe methodology consisted of a first experimental stage in which the modal rotations were measured with shearography and a subsequent numerical analysis in order to obtain the modal curvatures. To this end, three finite difference formulae were applied. The modal curvatures were then used to calculate the damage factors.FindingsIt was found that the profile of the damage factors varies according to the finite difference formula used. In view of the findings, the differences among the damage factors analysed are highlighted and some final recommendations to improve damage identifications via modal curvature-based are presented.Originality/valueTo the best of the authors’ knowledge, the application and comparison of several finite difference formulae and corresponding optimal sampling has not been carried out before. With the proposed approach, it is possible to identify multiple damages, which is still a great challenge. The post-processing of shearography measurements with a numerical method, which is inherently a multidisciplinary approach, is also a substantial improvement upon other type of approaches found in the literature.

A combined finite element and hierarchical Deep learning approach for structural health monitoring: Test on a pin-joint composite truss structure

Structural Health Monitoring (SHM) is an emerging field of engineering with a wide range of applications. The most common SHM strategies operate on structural responses through vibration measurements and focus on training mathematical classifiers which are used after to identify damage in unknown responses. Classifiers may additionally locate damage when adequate labeled damaged data is available. In the present work, a novel SHM method is presented where labeled damaged data is generated through FE models for a pin-joint composite truss structure employing a model-based approach for the problem of data acquisition. The truss is made of carbon fiber reinforced polymer (CFRP) members joint on aluminum connections forming a complex and large FE problem. A Deep Learning (DL) Convolutional Neural Network (CNN) classifier is trained on the FE generated vibration data combined with a hierarchical multiple damage identification and location scheme. The numerically trained CNN is after validated on experimental statuses of the truss in both damage detection and location, proving to be robust and accurate for the considered test case. The potential of hierarchical CNNs with FE based SHM data for multiple damages is investigated in this work and a comparison is given between hierarchical and direct multiclass CNNs. The large performance gains of the former are proven for the studied experimental case highlighting also the importance of SHM system architectures with CNNs.

Identification of Multiple Local Damage to an Offshore Jacket Substructure Using a Novel Strain Expansion–Reduction Approach

Modal parameter monitoring is a widely used structural health monitoring method. However, among other limitations, this method cannot effectively identify slight damage under ambient conditions. This study proposed a novel strain expansion–reduction approach for identifying damage. To verify the feasibility of the proposed method, we numerically and experimentally tested the method using a rigid acrylic frame. The frame was artificially damaged at various depths to reflect various damage scenarios. The increase in the damage index provided an accurate estimation of damage severity. For the case with merely 0.5% damage zone in one slat, the index is increased by 259% of the intact case. When the damage zone was doubled, the index increases significantly by 467% of the intact case, demonstrating excellent sensitivity of the proposed method. To guarantee practical use, the numerical model of the proposed method was applied to an offshore wind turbine jacket substructure and successfully identified multiple damage sites and the damage severity with extremely high (>10) damage index.

Wavelet analysis of static deflections for multiple damage identification in beams

Wavelet analysis can be used in local damage detection due to its ability of revealing discontinuities induced by damage in the displacement field. This paper focuses on the application of wavelet analysis to detect and identify multiple damages using the static deflection of beams. The local damages are located by the wavelet maxima lines and their severity are evaluated from a damage index obtained from the wavelet coefficients along the corresponding maxima lines. A series of experimental tests were conducted to examine the performance of the methodology for multiple damage scenarios. The static deflections of the beam were measured by a Digital Image Correlation system. As an application, a l1 regularization based filter is adopted to diminish the measurement noise which is critical in the application of wavelet analysis. The paper shows the capability of using wavelet analysis for closely spaced notch-type damage detection. It also analyzes the limits of the method in estimating damage with relative small severity in the presence of severe ones.

Multiple Damage Identification in Beams from Full-Field Digital Photogrammetry

AbstractThis paper addresses the identification problem of multiple open cracks in beams based on damage-induced variations in the static deflection of the beam. A two-step non-model-based damage l...

A Lamb wave signal reconstruction method for high-resolution damage imaging

In Lamb wave-based Structural Health Monitoring (SHM), a high-enough spatial resolution is highly required for Lamb wave signals to ensure the resolution and accuracy of damage detection. However, besides the dispersion characteristic, the signal spatial resolution is also largely restricted by the space duration of excitation waveforms, i.e., the Initial Spatial Resolution (ISR) for the signals before travelling. To resolve the problem of inferior signal spatial resolution of Lamb waves, a Lamb Wave Signal Reconstruction (LWSR) method is presented and applied for high-resolution damage imaging in this paper. In LWSR, not only a new linearly-dispersive signal is reconstructed from an original Lamb wave signal, but also the group velocity at the central frequency is sufficiently decreased. Then, both dispersion compensation and ISR improvement can be realized to achieve a satisfying signal spatial resolution. After the frequency domain sensing model and spatial resolution of Lamb wave signals are firstly analyzed, the basic idea and numerical realization of LWSR are discussed. Numerical simulations are also implemented to preliminarily validate LWSR. Subsequently, LWSR-based high-resolution damage imaging is developed. An experiment of adjacent multiple damage identification is finally conducted to demonstrate the efficiency of LWSR and LWSR-based imaging methods.

A robust locating multi-optima approach for damage identification of plate-like structures

This paper proposes a robust optimization approach for multiple damage identification of plate-like structures. Different from traditional particle swarm optimizations (PSOs), a combined PSO and niche technique (NPSO) is proposed to solve multimodal optimization problems, with the full consideration of subswarm creation, merging and absorbing mechanism. As a hypersensitive parameter to damage, the curvature mode shape is adopted to construct the objective function. Case studies are conducted to investigate the effectiveness and robustness of the algorithm on multi-damage identification. Simulation results show that the proposed algorithm exhibits robust search performance on identifying damage locations accurately with good convergence behavior. It is hoped that this study can provide guidance on robust damage detection, especially when the structure is subject to multiple damages and external disturbances.

Damage Classification for Masonry Historic Structures Using Convolutional Neural Networks Based on Still Images

AbstractManual inspection (i.e., visual inspection and/or with professional equipment) is the most predominant approach for identifying and assessing superficial damage of masonry historic structures at present. However, this method is costly and at times difficult to apply to remote structures or components. Existing convolutional neural network (CNN)‐based damage detection methods have not been specifically designed for the multiple damage identification of masonry historic structures. To overcome these limits, a deep architecture of CNN damage classification techniques for masonry historic structures is proposed in this article using a sliding window‐based CNN method to identify and locate four categories of damage (intact, crack, efflorescence, and spall) with an accuracy of 94.3%. This is the first attempt to identify the multidamage of historic masonry structures based on CNN techniques and achieve excellent classification results. The data are only trained and tested from images of the Forbidden City Wall in China, and the pixel resolutions of stretcher brick images and header brick images are 480 × 105 and 210 × 105, respectively. Two CNNs (AlexNet and GoogLeNet) are both trained on a small dataset (2,000 images for training, 400 images for validation and testing) and a large dataset (20,000 images for training, 4,000 images for validation and testing). The performance of the trained model (94.3% accuracy) is examined on five new images with 1,860 × 1,260 pixel resolutions.

Identification of multiple damage using modal rotation obtained with shearography and undecimated wavelet transform

Abstract Due to its high spatial resolution measurements and flexible and contactless experimental setup, shearography, as a non-destructive testing technique, provides effective structural damage identifications. However, this effectiveness can be increased by applying post-processing algorithms to experimentally measured modal rotation fields. In this paper, the authors present a novel method, consisting of shearographic non-destructive testing and 2D undecimated wavelet transform as a post-processing tool. The studies covered tests of the proposed method on simulated modal data of a damaged plate, as well as a validation on real data obtained from shearographic experiments. The results show the high sensitivity of the proposed method to either single or multiple damage, including its edges and its various configurations.

Non-model-based multiple damage identification of beams by a continuously scanning laser Doppler vibrometer system

An effective non-model-based multiple damage identification method for beams by using a continuously scanning laser Doppler vibrometer (CSLDV) system is presented. Velocity response of a beam along a scan line under sinusoidal excitation is measured by the CSLDV system and a spatially dense operating deflection shape (ODS) of the beam along the scan line is obtained by the demodulation method from velocity response. An ODS of an associated undamaged beam is obtained by using a polynomial with a proper order to fit the ODS from the demodulation method. The curvature of an ODS (CODS) is used to identify abnormality induced by multiple damage. A curvature damage index (CDI) using differences between CODSs associated with ODSs that are obtained by the demodulation method and the polynomial fit is proposed to identify multiple damage. A normalized average CDI obtained by averaging CDIs at different excitation frequencies is defined to further assist multiple damage identification. Experiments on three beams with three damage on each beam in the form of three small cuts are conducted. Widths and depths of the three damage are varied from 3mm to 9mm with an increment of 3mm and from 5% thickness reduction to 15% with an increment of 5%, respectively, and their effects on ODSs, CODSs, and CDIs are investigated. Five frequencies obtained by rounding the first through fifth natural frequencies of the beams and one arbitrarily selected frequency that is not close to any natural frequency of the beams are used as sinusoidal excitation frequencies. Each damage is successfully identified near regions with consistently high values of CDIs at different excitation frequencies. The three damage on each beam is successfully identified with the normalized average CDI by obvious peaks at locations of the three damage.

The use of quasi-isospectral operators for damage detection in rods

We consider the inverse problem of reconstructing the axial stiffness of a damaged rod from the knowledge of a finite number of resonant frequencies of the free axial vibration under supported end conditions. The damage is described as a reduction of the axial stiffness, and the undamaged and damaged configurations of the rod are assumed to be symmetric. The method is based on repeated determination of quasi-isospectral rod operators, that is rods which have the same spectrum of a given rod with the exception of a single resonant frequency which is free to move in a prescribed interval. The reconstruction procedure is explicit and it is numerically implemented and tested for the identification of single and multiple localized damages. The sensitivity of the technique to the number of frequencies used and to the shape, intensity and position of the damages, as well as to the presence of noise in the data, is evaluated and discussed. The effect of suitable filtering of the results based on a priori information on the physics of the problem is proposed. An experimental application to the identification of localized damage in a free-free steel rod is also presented.

Multiple Damage Identification in Beam Structure Based on Wavelet Transform

In this paper a damage identification algorithm for beam structures with multiple damage sites based on wavelet transform method of vibration mode shapes is reported. A complex morlet function of order 1-1 is chosen as a wavelet function. Wavelet transform coefficients serve as a damage indices which are standardized according to statistical hypothesis approach, yielding a standardized damage index distribution over beam coordinate. The peaks with the largest amplitude correspond to the zone of damage. Finite element simulations of proposed methodology involving various artificial noise levels and reduction of mode shape input data points are carried out. Results show that the algorithm is capable of capturing the areas of damage.

Baseline-free damage identification of metallic sandwich panels with truss core based on vibration characteristics

A baseline-free damage identification method is proposed to identify damages in metallic sandwich panels with truss core in the article. The method is based on flexibility matrix and gapped smoothing method, with damage index defined DIm. The weight coefficient m is introduced to consider the effect of damages on both low-order modes and high-order modes. Numerical simulations and experiments are conducted to evaluate the present method. Besides, damage index is also defined by processing DIm with Teager energy operator, and comparisons between DIm and are also carried out. Results show that the proposed method is effective in detecting single damage and multiple damages of the same or different extent. The weight coefficient m plays a very important role in identification of multiple damages of different styles. When comparing with , it is found that the present index DIm is better at suppressing the singularity caused by contact nodes and detecting of multiple damages which contain small or slight damages.

Multisensor Aggregation Algorithms for Structural Damage Diagnosis Based on a Substructure Concept

One of the important goals of structural health monitoring is damage detection. Although many methods have been proposed to detect the existence of structural damage, relatively few studies are found on higher-level damage diagnosis such as identification of the location and extent of damage. In this paper, multiple substructural damage identification models based on regression between internal responses and boundary responses of individual beam elements in either plane or three-dimensional space are derived. Three damage indexes are defined from regression model characteristics, and two change-point analysis methods are adopted to capture changes in damage index sequences which are extracted from structural monitoring data sets from healthy and unknown states. Possible damage locations are identified as where the most significant changes in the damage indexes occur, and a voting scheme is used to synthesize the results from different algorithms. This damage detection approach is straightforward and efficient, with the regression coefficients directly related to the structural stiffness properties. The numerical and experimental application results show that the method successfully identifies and locates structural change in most of the cases.

Identification of multiple damage in beams based on robust curvature mode shapes

Multiple damage identification in beams using curvature mode shape has become a research focus of increasing interest during the last few years. On this topic, most existing studies address the sensitivity of curvature mode shape to multiple damage. A noticeable deficiency of curvature mode shape, however, is its susceptibility to measurement noise, easily impairing its advantage of sensitivity to multiple damage. To overcome this drawback, the synergy between a wavelet transform (WT) and a Teager energy operator (TEO) is explored, with the aim of ameliorating the curvature mode shape. The improved curvature mode shape, termed the TEO-WT curvature mode shape, has inherent capabilities of immunity to noise and sensitivity to multiple damage. The efficacy of the TEO-WT curvature mode shape is analytically verified by identifying multiple cracks in cantilever beams, with particular emphasis on its ability to locate multiple damage in noisy conditions; the applicability of the proposed curvature mode shape is experimentally validated by detecting multiple fairly thin slots in steel beams with mode shapes acquired by a scanning laser vibrometer. The proposed curvature mode shape appears sensitive to multiple damage and robust against noise, and therefore is well suited to identification of multiple damage in beams in noisy environments.

Damage Identification of Multimember Structure using Improved Neural Networks

A novel two stage Improved Radial Basis Function (IRBF) neural network for the damage identification of a multimember structure in the frequency domain is presented. The improvement of the proposed IRBF network is carried out in two stages. Conventional RBF network is used in the first stage for preliminary damage prediction and in the second stage reduced search space moving technique is used to minimize the prediction error. The network is trained with fractional frequency change ratios (FFCs) and damage signature indices (DSIs) as effective input patterns and the corresponding damage severity values as output patterns. The patterns are searched at different damage levels by Latin hypercube sampling (LHS) technique. The performance of the novel IRBF method is compared with the conventional RBF and Genetic algorithm (GA) methods and it is found to be a good multiple member damage identification strategy in terms of accuracy and precision with less computational effort.

Fast damage imaging using the time-reversal technique in the frequency–wavenumber domain

The time-reversal technique has been successfully used in structural health monitoring (SHM) for quantitative imaging of damage. However, the technique is very time-consuming when it is implemented in the time domain. In this paper, we study the technique in the frequency–wavenumber (f–k) domain for fast real-time imaging of multiple damage sites in plates using scattered flexural plate waves. Based on Mindlin plate theory, the time reversibility of dispersive flexural waves in an isotropic plate is theoretically investigated in the f–k domain. A fast damage imaging technique is developed by using the cross-correlation between the back-propagated scattered wavefield and the incident wavefield in the frequency domain. Numerical simulations demonstrate that the proposed technique cannot only localize multiple damage sites but also potentially identify their sizes. Moreover, the time-reversal technique in the f–k domain is about two orders of magnitude faster than the method in the time domain. Finally, experimental testing of an on-line SHM system with a sparse piezoelectric sensor array is conducted for fast multiple damage identification using the proposed technique.