Structural Health Monitoring Techniques Research Articles

Subsurface impact damage imaging for composite structures using 3D digital image correlation

An integrated system is proposed to visualize subsurface barely visible impact damage (BVID) in composite structures using three-dimensional (3D) digital image correlation (3D DIC). This system uses a pair of digital cameras to record video frames in the field-of-view (FOV) of the structure’s surface, capturing the wavefield generated via chirp excitation in the near-ultrasonic frequency range. Significant pitfalls of previous efforts of damage imaging using two-dimensional DIC have been largely mitigated. First, 3D DIC enables capturing out-of-plane displacements, which are much larger in amplitude versus in-plane displacements that a single camera would be limited to sensing, thus increasing the signal-to-noise ratio. This enhancement in turn increases the sensitivity of the stereo-camera system. Second, a total wave energy (TWE) damage imaging condition is proposed to visualize the local damage region. The monogenic signal obtained via Reisz transform (RT) is employed to compute the instantaneous amplitude, with which the local wave energy can be calculated spatially over time. Since a high displacement amplitude and thus high wave energy will occur in the damage region due to the local resonance, the proposed TWE imaging condition can relax the Nyquist sampling requirement, unlike guided-wave-based structural health monitoring techniques which require fully reconstructing the wavefield and wave modes through sampling that satisfies the Nyquist criterion. As such, a much lower camera frame rate is adequate for the proposed system. Consequently, the maximum spatial resolution of the camera for a given FOV can be achieved at the expense of a reduced frame rate. With the maximized pixel resolution and reduced frame rate for employing the TWE imaging condition, composite structures can be inspected or monitored with a larger FOV. As a result, there is no longer any need to apply signal enhancement techniques, such as sample interleaving, image stitching, or averaging, to increase the effective performance of the camera. Rather than needing thousands of repeated videos for minimizing the incoherent noise, only a single stereo-video with a few seconds of sampling duration is necessary for damage imaging. The use of a powerful piezo-shaker also increases the wave signal amplitude and further enhances sensitivity without permanent adhesion. To demonstrate this stereo-camera concept with the TWE imaging condition, the system was used to image damage in two carbon fiber reinforced polymer composite honeycomb panels, which had been subjected to low-velocity impacts (2 J). For each panel, two excitation configurations were used to verify the robustness of the system. Initial damage maps produced for a 100 × 100-mm FOV using a three-second stereo-video show accurate damage imaging ability that is independent of excitation location and comparable to benchmark damage images computed from laser Doppler vibrometer data and those gathered from ultrasonic and X-ray computerized tomography scans. This efficient and reliable integrated system demonstrated high potential for in-time damage inspection on composite aircraft and other critical structures.

Probabilistic Approach for Electromechanical Impedance-Based SHM Models

Applying structural health monitoring techniques has become very important in engineering and aeronautics. The technique based on electromechanical impedance is one of them. In this work, the electromechanical impedance-based SHM technique is applied to determine the existence of damage in an aluminum beam using the Root Square Deviation (RMSD) damage metric. Furthermore, this contribution aims to apply the concept of stochastic evaluation in the context of the electromechanical impedance-based SHM technique by using the Monte Carlo method to generate random numbers. The development of the work is to validate two models, one with the impedance signals and the other stochastically, and thus build the linear regression model for both. In the end, the results are compared to test the representation of the failure identification in the beam.

Numerical approach to disbonds in bonded composite Single Lap Joints: Comparison between Carrera Unified Formulation and classical Finite Element modeling

The scope of this study is the monitoring of the adhesion integrity of composite Single Lap Joints (SLJs) through the assessment of the MUL2 CODE, software developed by the MUL2 Research Group - Department of Mechanical and Aerospace Engineering of Politecnico di Torino. The MUL2 CODE is implemented through the Carrera Unified Formulation (CUF) for 2D structures based on Hierarchical Legendre Expansion (HLE) polynomials. An efficient method for the Structural Health Monitoring (SHM) of bonded composite joints is experimentally verified and simulated by commercial FE software and by the CUF approach, in order to reduce the computational cost of analyses: using transient excitations via toneburst signals, the structural health of damaged SLJ is analytically, experimentally and numerically evaluated. The interaction mechanism between the waves traveling through the investigated specimens is numerically simulated with a simple Finite Elements (FE) model and it is solved via MUL2 CODE and commercial software Ansys Workbench (with 2D and 3D models), respectively. Experimental campaigns data are compared with analytical, CUF and Ansys results demonstrating the consistence of the MUL2 formulation that is computationally simpler, but very efficient for the joint analysis. The presented and discussed CUF application is able to quantify with a high accuracy the debonding extension in the damaged SLJ, simply tuning the excitation frequency of the SHM technique.

Influence of void damage on the electromechanical impedance spectra of Single Lap Joints

Adhesively bonded joints are susceptible to a variety of damage sources, which may be of difficult detection with current Non-Destructive Testing (NDT) methods. Structural Health Monitoring (SHM) techniques, such as the Electromechanical Impedance Spectroscopy (EMIS), aims to outpace NDT by continuously monitoring structures for defects. However, given the complexity of adhesive joint structures, EMIS capability for damage detection still needs to be thoroughly studied. In this manner, electrical impedance measurements were done to Single Lap Joints (SLJ), with both a modified epoxy adhesive and Pressure Sensitive Adhesive (PSA) for void detection. Void defects could not be detected on adhesive joints with the PSA but, in the case of joints with the modified epoxy adhesive, impedance signatures were significantly altered by the presence of both the artificially created void, and by a second damage source caused during the SLJ manufacture. Experimental measurements from modified epoxy joints were compared with simulation results, yielding good approximation when considering the effect of the second source of damage. An approximation of the mechanical impedance was also computed using numerical results from the pristine SLJ model. In this manner, it is possible to detect damage in the adhesive layer of SLJs with electrical impedance measurements from EMIS.

A modified hyperbola based baseline free approach using Lamb wave mode conversion for detection of multiple structural damages: a simulation based study

Guided wave based techniques are among the promising techniques for structural health monitoring due to their ability to detect damage with high precision, ability to scan large area and low power consumption. However, practical implementation of these techniques to structures with relatively complex geometries such as stiffened plates demand further research due to several challenges. Some of such challenges include the presence of stiffeners and edges in close vicinity causing multiple reflections of waves, possibility of multiple damages and lack of baseline data. To address all these issues, a modified hyperbola based approach using mode converted signal and a data based matching scheme has been proposed in this paper. A stiffened plate is taken as the example structure. Corrosion like defects have been considered as the damages. Each panel of the stiffened plate has been scanned separately through phased array actuation. The effectiveness and robustness of the proposed approach have been shown through a case study using 3D finite element simulation data.

Deep Autoencoder Thermography for Defect Detection of Carbon Fiber Composites

Infrared thermography (IRT) is an economical nondestructive testing technique for structural health monitoring of composite materials. However, the nonlinear nature of the thermographic data and the adverse effects of noise and inhomogeneous backgrounds prevent it from achieving satisfactory results. Most of the existing thermographc data analysis methods are supervised and/or linear, which therefore are not favorable for nonlinear feature extraction of unlabeled thermograms. In this work, a deep autoencoder thermography (DAT) method is proposed for detecting subsurface defects in composite materials. The multi-layer network structure of DAT can handle nonlinear temperature profiles, and the output of the intermediate hidden layer is visualized to highlight defects. The layer-by-layer feature visualization reveals how the model extracts defect features. A loss inflection point scheme is utilized to determine a suitable depth of the model. Moreover, a new quantitative index is proposed to compare the defect detectability of different methods.

On the influence of the probabilistic microstructural characteristics of glass fiber‐reinforced composites on the wave propagation in GLARE

AbstractIn an attempt to reduce the crack propagation speed in structures made of fiber‐reinforced composites metal layers are added to the laminate forming a new group of materials, so‐called fiber metal laminates. GLARE, a combination of glass fiber‐reinforced polymer and aluminum, is well‐known and frequently used in the aeronautical industry. One major drawback of such materials is their susceptibility to impact damage, which is not detectable by visual inspections. Hence, an adequate structural health monitoring technique to detect interlaminar damage like delaminations is essential for components made from fiber metal laminates. One approach here is the use of guided ultrasonic waves because they travel long distances without substantial damping and cover the whole thickness of thin‐walled structures. However, the probabilistic microstructure causes a continuous mode conversion in wave guides made from fiber‐reinforced composites. This significantly influences the signal processing and as a consequence, the interpretation of captured time signals. This phenomenon is induced by the random spatial distribution of the fibers in the matrix material, leading to an ongoing excitation of new wave modes within the propagating S0 wave mode. In this research, the effect is studied for the wave propagation in GLARE 3/2‐0.4. Therefore, the spatial distribution of the transverse YOUNG's modulus for the glass fiber‐reinforced layers is represented by homogeneous second‐order GAUSSian random fields. Afterwards, the propagation of the guided ultrasonic wave is simulated numerically. The results reveal that the continuous mode conversion is not only observable within the glass fiber‐reinforced polymer layers but also at the top and bottom surface and hence, within the isotropic layers. This holds for the wave propagation both in and perpendicular to the fiber orientation of the glass fiber‐reinforced polymer layers.

Development and Validation of a LabVIEW Automated Software System for Displacement and Dynamic Modal Parameters Analysis Purposes

The structural health monitoring (SHM) technique is a highly competent operative process dedicated to improving the resilience of an infrastructure by evaluating its system state. SHM is performed to identify any modification in the dynamic properties of an infrastructure by evaluating the acceleration, natural frequencies, and damping ratios. Apart from the vibrational measurements, SHM is employed to assess the displacement. Consequently, sensors are mounted on the investigated framework aiming to collect frequent readings at regularly spaced time intervals during and after being induced. In this study, a LabVIEW program was developed for vibrational monitoring and system evaluation. In a case study reported herein, it calculates the natural frequencies as well as the damping and displacement parameters of a cantilever steel beam after being subjected to excitation at its free end. For that purpose, a Bridge Diagnostic Inc. (BDI) accelerometer and a displacement transducer were parallelly mounted on the free end of the beam. The developed program was capable of detecting the eigenfrequencies, the damping properties, and the displacements from the acceleration data. The evaluated parameters were estimated with the ARTeMIS modal analysis software for comparison purposes. The reported response confirmed that the proposed system strongly conducted the desired performance as it successfully identified the system state and modal parameters.

Performance assessment of steel cantilever beams based on connection behaviour using DIC technique and improved hybrid PSO algorithm

Cantilever beams can have various behaviour depending on the details of the connection. Partially-fixed connections have a different performance from conventionally-fixed connections, in which the exact information is not available. In this paper, using the finite element model updating (FEMU) of a steel cantilever beam, the support conditions of a partially-fixed connection and the behaviour of the beam are investigated. The FEMU is utilized as an appropriate technique for structural health monitoring, which requires the use of data acquisition methods from the structure. In this research, instead of using discrete sensors, displacement and strain information are continuously measured from the beam surface using the digital image correlation (DIC) method. The difference between the initial model and the data extracted from the DIC is determined by a cost function. A hybrid method called Powell particle swarm optimization is used to minimize the cost function. This method includes particle swarm optimization (PSO) as a global search technique and Powell optimization as a local search technique; the hybrid method achieves better performance by combining the two methods. A dynamic inertia weight is presented to improve the performance of the PSO algorithm. Abaqus software and Python programming have been used to carry out the FEMU process. Finally, the amount of rotation and displacement of support are calculated, and the dependency of beam behaviour on each of the variables is investigated. This method yields acceptable convergence of the results and can be extended to study other structural components.

Guided Wave Damage Location of Pressure Vessel Based on Optimized Explainable Convolutional Neural Network for Multivariate Time Series Classification Neural Network

Abstract Guided wave is a key nondestructive technique for structural health monitoring due to its high sensitivity to structural changes and long propagation distance. However, to achieve high accuracy for damage location, large quantities of samples and thousands of iterations are typically needed for detection algorithms. To address this, in this paper, an eXplainable Convolutional neural network for Multivariate time series classification (XCM) is adopted, which is composed of one-dimensional (1D) and two-dimensional (2D) convolution layers to achieve high accuracy damage location on pressure vessels with limited training sets. By further optimizing the network parameters and network structure, the training time is greatly reduced and the accuracy is further improved. The optimized XCM improves the damage location precision from 95.5% to 98% with small samples (training set/validation set/testing set = 23/2/25) and low training epochs (under 100 epochs), suggesting that the XCM has great advantages in pressure vessel's damage location classification its potential for guided wave-based damage detection techniques in structural health monitoring.

An Efficient Lightweight Deep-Learning Approach for Guided Lamb Wave-Based Damage Detection in Composite Structures

Woven fabric composite structures are applied in a wide range of industrial applications. Composite structures are vulnerable to damage from working in complex conditions and environments, which threatens the safety of the in-service structure. Damage detection based on Lamb waves is one of the most promising structural health monitoring (SHM) techniques for composite materials. In this paper, based on guided Lamb waves, a lightweight deep-learning approach is proposed for identifying damaged regions in woven fabric composite structures. The designed deep neural networks are built using group convolution and depthwise separated convolution, which can reduce the parameters considerably. The input of this model is a multi-channel matrix transformed by a one-dimensional guided wave signal. In addition, channel shuffling is introduced to increase the interaction between features, and a multi-head self-attention module is designed to increase the model’s global modeling capabilities. The relevant experimental results show that the proposed SHM approach can achieve a recognition accuracy of 100% after only eight epochs of training, and the proposed LCANet has only 4.10% of the parameters of contrastive SHM methods, which further validates the effectiveness and reliability of the proposed method.

Sensor-Based Calibration of Camera’s Extrinsic Parameters for Stereophotogrammetry

Stereophotogrammetry is a well-recognized technique for structural health monitoring. Before performing any quantitative stereophotogrammetry measurement, the cameras must be calibrated to obtain the intrinsic and extrinsic parameters of the stereovision system. However, when large-sized structures are to be monitored, the calibration process is challenging and limits the use of stereophotogrammetry. In this article, a sensor-based calibration method for determining the extrinsic parameters of a stereovision system is presented and validated. A multisensor board has been developed that synchronizes inertial measurement units (IMUs) and a laser on a single board computer to measure the spatial orientation and the distance of two paired cameras and compute the extrinsic parameters of the stereovision system. The effectiveness of the sensor-based calibration is evaluated through both analytical studies to quantify the effects of performance degradation caused by the sensors’ noise as well as laboratory tests. Results show that the sensor-based calibration is effective in quantifying displacement with errors below 3% when compared to measurements performed using a stereovision system calibrated with the traditional image-based procedure.

Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review

The development of Structural Health Monitoring (SHM) and Non-Destructive Testing (NDT) techniques has rapidly evolved and matured over the past few decades. Advances in sensor technology have facilitated deploying SHM systems for large-scale structures and local NDT of structural members. Although both methods have been successfully applied to identify structural damage in various systems, Environmental and Operational Condition (EOC) variations can influence sensor measurements and mask damage signatures in the structural response. EOCs include environmental conditions, such as temperature, humidity, and wind, as well as operational conditions, such as mass loading, vibration, and boundary conditions. The effect of EOCs can significantly undermine the reliability and robustness of damage assessment technologies and limit their performance. Thus, successful SHM and NDT systems can compensate for changing EOCs. This paper provides a state-of-the-art review of the effects of EOCs on SHM and NDT systems. It presents recent developments in advanced sensing technology, signal processing, and analysis techniques that aim to eliminate the masking effect of EOC variations and increase the damage sensitivity and performance of SHM and NDT systems. The paper concludes with current research challenges, trends, and recommendations for future research directions.

State-of-the-art ensemble learning and unsupervised learning in fatigue crack recognition of glass fiber reinforced polyester composite (GFRP) using acoustic emission

Fatigue strength is one of the most important properties of composite materials because it directly relates to their lifespan. Acoustic emission (AE) is a passive structural health monitoring (SHM) technique that provides real-time damage detection based on stress waves generated by cracks in the structure. This study evaluates the damage progression on glass fiber reinforced polyester composite specimens using different approaches of machine learning. Different methodologies for damage detection and characterization of AE parameters are presented. Three different ensemble learning methods namely, XGboost, LightGBM, and CatBoost were chosen to predict damages and AE parameters. SHAP values were used to select AE key features and K-means algorithms were employed to classify damage severity. The accuracy of these approaches demonstrates the reliability of various machine learning techniques in predicting the fatigue life of composite materials using acoustic emission.

Debonding quantification in adhesive bonded joints by the inverse finite element method

In the past two decades, the aerospace industry has massively shifted from aluminum-made components to composite materials such as carbon fiber reinforced polymers (CFRP), striving for more fuel efficient and lighter aircrafts. Consequently, traditional joints have been replaced by adhesive bonded interfaces, which are also the most common choice to repair damaged components. Although adhesive bonding is the most efficient choice for permanent connections, it is not free of disadvantages: one of the most common failure modes, the debonding of the two laps, is very problematic to detect and predict in practice. Therefore, frequent inspections must be performed to ensure structural safety, increasing maintenance costs, and lessening the availability of the platforms. The development of innovative sensing technologies has allowed for a close monitoring of structural interfaces, and several structural health monitoring techniques have been proposed to monitor adhesive bonded connections. Sensitivity and correlation between measurements and debonding entity has been demonstrated in the literature: nevertheless, hardly any technique has been proposed and quantitively evaluated to estimate the debonding entity independently of the applied loads, such as misalignment-induced torsion, which is a major confounding influence in the traditional backface strain gauge technique. This paper proposes the inverse finite element method (iFEM) as a load and material independent approach to infer the debonding entity from strain measurements in adhesive-bonded joints. Two approaches to estimate the debonding entity with the iFEM are compared on cracked leap shear specimens representative of CFRP repair patches: one is based on anomaly indexes, the other on performing a model selection with multiple iFEM models including different damages. The latter demonstrates satisfactory performances; thus, it is considered a significant scientific advancement in this field.

Satellite SAR Interferometry and On-Site Traditional SHM to Monitor the Post-Earthquake Behavior of the Civic Tower in L’Aquila (Abruzzo Region, Italy)

Structural Health Monitoring (SHM) represents a very powerful tool to assess the health condition of buildings. In recent years, the growing availability of high-resolution SAR satellite images has made possible the application of multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques for structural monitoring purposes, with high precision, low costs, timesaving, and the possibility to investigate wide areas. However, a comprehensive validation of the effectiveness of MT-InSAR in this application field has not been achieved yet. For this reason, in this paper a comparison between interferometric data and on-site measurement of displacements is proposed. The application case study is the Civic Tower of the city of L’Aquila (Abruzzo Region, Italy). After the seismic events that affected the area in 2009, an on-site monitoring system was installed on the tower to detect any changes in the damage pattern in the period 2010–2013. Furthermore, images acquired by COSMO-SkyMed constellation in Stripmap mode (~3 m resolution) during the same period were processed by the Permanent Scatterer-InSAR (PSI) technique to estimate the deformation of the structure and the surrounding area. The obtained results indicate that both methods are consistent in the measurement of displacement trends of the building and a slight rotation/displacement of the tower was detected. Such evidence highlights both the huge potential and the limitations of using InSAR techniques for SHM.

Self-learning locally-optimal hypertuning using maximum entropy, and comparison of machine learning approaches for estimating fatigue life in composite materials of the aerospace industry

Applications of Structural Health Monitoring (SHM) combined with Machine Learning (ML) techniques enhance real-time performance tracking and increase structural integrity awareness of civil, aerospace and automotive infrastructures. This SHM-ML synergy has gained popularity in the last years thanks to the anticipation of maintenance provided by arising ML algorithms and their ability of handling large quantities of data and considering their influence in the problem.In this paper we develop a novel ML nearest-neighbors-alike algorithm based on the principle of maximum entropy to predict fatigue damage (Palmgren–Miner index) in composite materials by processing the signals of Lamb Waves – a non-destructive SHM technique – with other meaningful features such as layup parameters and stiffness matrices calculated from the Classical Laminate Theory (CLT). The full data analysis cycle is applied to a dataset of delamination experiments in composites. The predictions achieve a good level of accuracy, similar to other ML algorithms, e.g. Neural Networks or Gradient-Boosted Trees, and computation times are of the same order of magnitude.The key advantages of our proposal are: (1) The automatic determination of all the parameters involved in the prediction, so no hyperparameters have to be set beforehand, which saves time devoted to hypertuning the model and also represents an advantage for autonomous, self-supervised SHM. (2) No training is required, which, in an online learning context where streams of data are fed continuously to the model, avoids repeated training—essential for reliable real-time, continuous monitoring.

Localizing damage on stainless steel structures using acoustic emission signals and weighted ensemble regression-based convolutional neural network

Nuclear power generation is an essential part of the electrical supply in the United States, and it is an effective way to achieve low carbon power generation. Nuclear power generation produces spent radioactive fuel. If improperly disposed of or improperly stored, spent fuel can affect the environment and human health. Currently, in the United States, spent fuel is typically stored in stainless steel canisters. Some stainless steel canisters deployed in coastal areas are subject to stress corrosion cracks. Long-term monitoring and to provide timely maintenance of the storage canisters is necessary to prevent leakage of spent fuel due to damaged canisters. Acoustic emission (AE) is a structural health monitoring (SHM) technique that can be utilized to monitor large-scale metallic structures because it is extremely sensitive to damage initiation and propagation in materials. However, the challenge in using AE is in deploying a minimal number of AE sensors on a canister due to cost and environmental restrictions while still being able to precisely detect and localize damage formation. The innovation of this paper lies in the development of an automated damage localization method to estimate the coordinates of damage by using a single AE sensor. A data fusion approach was designed to integrate the information from waveforms, fast Fourier transform (FFT) spectrums, and spectrum entropy and then convert the AE signals into three types of images along with short-time Fourier transform (STFT) and continuous wavelet transforms (CWT). A weighted ensemble regression-based convolutional neural network was proposed to analyze the images and compute the coordinates of damage. The proposed method was validated on a large-scale steel plate specimen that simulate the canister, and three-fold cross-validation was conducted to ensure the method was effectively evaluated. The results suggest that the proposed method has a high performance rate for locating damage.

Noncontact Sensing Techniques for AI-Aided Structural Health Monitoring: A Systematic Review

Engineering structures and infrastructure continue to be used despite approaching or having reached their design lifetime. While contact-based measurement techniques are challenging to implement at a large scale and provide information at discrete locations only, noncontact methods are more user-friendly and offer accurate, robust, and continuous spatial information to quantify the structural conditions of the targeted systems. Advancements in optical sensors and image-processing algorithms increased the applicability of image-based noncontact techniques, such as photogrammetry, infrared thermography, and laser imaging for structural health monitoring (SHM). In addition, with the incorporation of artificial intelligence (AI) algorithms, the assessment process is expedited and made more efficient. This article summarizes the efforts made in the last five years to leverage AI-aided noncontact sensing techniques for applications in SHM with an emphasis on image-based methods. Future directions to advance AI-aided image-based sensing techniques for SHM of engineering structures are also discussed.

A review on fatigue monitoring of structures

PurposeThe purpose of this study is to review the existing fatigue and vibration-based structural health monitoring techniques and highlight the advantages of combining both approaches.Design/methodology/approachFatigue monitoring requires a fatigue model of the material, the stresses at specific points of the structure, a cycle counting technique and a fatigue damage criterion. Firstly, this paper reviews existing structural health monitoring (SHM) techniques, addresses their principal classifications and presents the main characteristics of each technique, with a particular emphasis on modal-based methodologies. Automated modal analysis, damage detection and localisation techniques are also reviewed. Fatigue monitoring is an SHM technique which evaluate the structural fatigue damage in real time. Stress estimation techniques and damage accumulation models based on the S-N field and the Miner rule are also reviewed in this paper.FindingsA vast amount of research has been carried out in the field of SHM. The literature about fatigue calculation, fatigue testing, fatigue modelling and remaining fatigue life is also extensive. However, the number of publications related to monitor the fatigue process is scarce. A methodology to perform real-time structural fatigue monitoring, in both time and frequency domains, is presented.Originality/valueFatigue monitoring can be combined (applied simultaneously) with other vibration-based SHM techniques, which might significantly increase the reliability of the monitoring techniques.