Damage Localization Algorithm Research Articles

A novel damage localization technique for type III composite pressure vessels based on guided wave mode-matching method

Damage monitoring during the service life of Type III composite overwrapped pressure vessels (COPVs) is crucial for ensuring their safe operation. This paper focuses on the damage localization in COPVs using ultrasonic guided waves structural health monitoring (SHM) techniques. Firstly, dispersion curves were plotted to establish a foundation for experiments and simulations based on the propagation theory of guided waves in multilayered anisotropic structures. Subsequently, a 3D finite element model of COPV was constructed to capture the propagation characteristics of guided waves within the COPV and their interaction with damage. The effects of different fiber angles and fiber layer numbers on guided waves propagation were analyzed, and their interrelationships were established. Furthermore, the “Wave Velocity Directionality” effect of the A0 mode was identified during its propagation in COPV. The monitoring signals obtained from experiments were analyzed to assess the impact of damage on the time-domain and frequency-domain signals of guided waves. Finally, a damage localization algorithm based on mode-matching was proposed, and its localization accuracy was verified in COPVs with different damage locations and fiber layer numbers. The results demonstrate the significant potential of the proposed damage localization algorithm in the damage monitoring of COPV structures.

Research on Delamination Damage Localization of Carbon Fiber-Reinforced Polymer Curved Plate Using Lamb Wave

Carbon fiber-reinforced polymers (CFRPs) are extensively employed in the aerospace industry due to their excellent properties. Delamination damage occurring at critical locations in CFRPs can seriously reduce the safety of in-service components. The detection and localization of delamination damage using Lamb waves hold significant potential for widespread application in non-destructive testing. However, the choice of damage localization algorithm may produce different delamination damage localization results. This research presented an IRAPID (improved reconstruction algorithm for probabilistic inspection of defects) method derived from the RAPID (reconstruction algorithm for probabilistic inspection of defects) method, aiming to improve the accuracy and reliability of delamination damage localization. Three CFRP curved plates, including a healthy curved plate and two curved plates with delamination damage sizes of Φ20 mm and Φ40 mm, were prepared in the experiment. The detection experiment of the CFRP curved plate using lead zirconate titanate (PZT) as a transducer to excite and receive Lamb waves was conducted, and the influence of excitation signal frequency on the performance of the proposed method was discussed. Under the condition of an excitation signal frequency of 220~320 kHz and a step size of 10 kHz, the accuracy of the delamination damage localization method proposed in this paper was compared with that of existing methods. The experimental results indicate that the IRAPID algorithm exhibits good stability in the localization of delamination damage across the range of frequency variations considered. The localization error of the IRAPID algorithm for delamination damage is significantly lower than that of the DaS (delay-and-sum) algorithm and the RAPID algorithm. As the size of the delamination damage increases, so does the localization error. The accuracy of delamination damage localization is lower in the X-axis direction than in the Y-axis direction. By averaging the localization results across various frequencies, we can mitigate the potential localization errors associated with single-frequency detection to a certain extent. For the localization of delamination damage, Lamb waves at multiple frequencies can be employed for detection, and the detection results at each frequency are averaged to enhance the reliability of localization.

Bayesian approach of elliptical loci and RAPID for damage localization in wind turbine blade

This investigation addresses the issue of damage detection and localization in wind turbine blade laminates. This paper proposes a novel approach that integrates the elliptical trajectory and probabilistic imaging method using the Bayesian framework. This method employs multiple damage-sensitive features to enhance the reliability and robustness of sensor arrays. The algorithm is optimized by analyzing the propagation characteristics of Lamb waves in composite blade laminates. A numerical simulation is conducted on a 1.5 MW wind turbine blade laminate model, incorporating the scattered wave signal, wave arrival time, and correlation coefficient as damage characteristic signals. Markov Chain Monte Carlo sampling method is adopted to obtain the posterior distribution of the damage location and achieve accurate localization of blade delamination damage. The experimental results indicate that the damage localization algorithm, which utilizes the Bayesian approach, achieves an accuracy of approximately 97.04% in localizing delamination damage in blade laminates.

Rapid positioning test method for cylindrical structure penetration based on machine learning

Focusing on the requirements of rapid damage location test for multi-layer steel plates penetration of cylindrical structure in the complex and harsh environment, this paper studies the propagation characteristics of high-speed impact stress waves in steel plates. Also, the measurement effects of impact stress in the near zone of penetration hole are compared with different sensors, so as to select appropriate PZT sensors to measure the velocity of impact stress wave in the actual steel plates. Then the array layout scheme is designed, and the corresponding damage location algorithm is determined. Finally, the deep learning algorithm is used to improve the accuracy and efficiency of damage location.

An automated vibration-based structural damage localization strategy using filter-type feature selection

Damage detection techniques have been widely explored over the last years driven by the advances of computational intelligence technologies. To understand the structure’s dynamic behavior, some studies use modal-based methods, which are traditionally applied to reflect changes caused by damage. More recently, strategies using raw vibration measurements have gained notoriety and are proving to be a promising approach. In this context, the aim of this paper is to present a novel automated methodology for damage localization based on the extraction of features from dynamic data using domain knowledge and on an unsupervised filtering process. Feature extraction is performed simultaneously in time, frequency, and quefrency domains to diversify information retrieval. In machine learning (ML), this filtering procedure is called feature selection (FS) and is applied herein with the goal to decrease redundancy and increase the relevance of the feature set by eliminating a portion of it in view of a predefined criterion. The main concept is that the proposed method can customize its features concerning the structure, providing generality about any type of geometry, material, and excitation it encounters. The damage-sensitive index is computed through an outlier analysis, where percentile intervals are created based on the filtered features from the structure’s healthy state. Thus, any anomalies surrounding this initial state should be automatically located. The method was successfully tested in five applications, two of them being full-scale bridges, which shows a promising performance for real-world monitoring situations. Some of its significant achievements are single and multiple damage location, structural reinforcement detection, quantification of severity in progressive damage scenarios, and robustness to noise due to varying traffic and/or environmental conditions. Such a reliability is tied to an efficient default parameters setup, enabling an automated damage localization algorithm.

Unsupervised learning framework for temperature compensated damage identification and localization in ultrasonic guided wave SHM with transfer learning

Damage localization algorithms for ultrasonic guided wave-based structural health monitoring (GW-SHM) typically utilize manually-defined features and supervised machine learning on data collected under various conditions. This scheme has limitations that affect prediction accuracy in practical settings when the model encounters data with a distribution different from that used for training, especially due to variation in environmental factors (e.g., temperature) and types of damages. While deep learning based models that overcome these limitations have been reported in literature, they typically comprise of millions of trainable parameters. As an alternative, we propose an unsupervised approach for temperature-compensated damage identification and localization in GW-SHM systems based on transferring learning from a convolutional auto encoder (TL-CAE). Remarkably, without using signals corresponding to the damages during training (unsupervised), our method demonstrates more accurate damage detection and localization as well as robustness to temperature variations than supervised approaches reported on the publicly available Open Guided Waves (OGW) dataset. Additionally, we have demonstrated reduction in number of trainable parameters using transfer learning (TL) to leverage similarities between time-series among various sensor paths. It is also worth noting that the proposed framework uses raw time-domain signals, without any pre-processing or knowledge of material properties. It should therefore be scalable and adaptable for other materials, structures, damages, and temperature ranges, should more data become available in the future. We present an extensive parametric study to demonstrate feasibility of the proposed method.

Lamb wave-based damage assessment for CFRP composite structures using a CHMM-based damage localization algorithm and a damage quantitative expression

Accurately and quantitatively monitoring the damages in composite structures is very challenging, but important for assessing their integrity. In this paper, an innovative damage location identification and size quantification assessment method based on Lamb wave is proposed for composite laminates. A continuous hidden Markov model (CHMM)-based damage scaling factor together with a weighted average imaging algorithm is adopted in damage location identification. To quantify the degree of structural deviation from the healthy state, the inconspicuous change of guided wave (GW) signal is converted to a CHMM-based damage scaling factor with significant change. Based on the damage scaling factors of all GW sensing paths, a weighted average imaging algorithm is performed to identify the most probable damage location. In addition, a damage quantitative mathematical expression is proposed to approximately estimate the size of circular-shaped damage founded on several different damage-related characterizations. The implementation of the proposed quantitative methodology is comparatively simple and straightforward for damage detection. Experiments on three composite panels with multiple damage cases are conducted to substantiate the proposed method. The sensor layers with embedded piezoelectric transducer (PZT) sensor networks surf-mounted on the structures are employed to excite and receive Lamb wave signals since their superiority of flexibility, electrical stability and apt for complex structures. The results demonstrate that the proposed algorithm is capable of pinpointing both the location and size of the circular-hole damage in composite structures.

Damage Location Monitoring of Graphene/Conducting Polymer Composites Film Based on Self-Sensing.

Conductive graphene polymer composites are considered promising functional materials in gas detection, strain detection, metal corrosion prevention, and electromagnetic wave absorption, owing to their good flexibility, lightweight, and adjustable conductivity. The internal defects or external damages of composite films will seriously affect the electrical and functional properties of the materials. Based on the conductive network inside the conductive polymer film and the self-inductance to ultrasonic wave, the defect self-monitoring system of the conductive polymer film is designed and optimized in this work. The self-damage detection system is composed of an electrode array, excitation source, resistance signal acquisition and processing circuit, and damage display. Aiming at different scenarios, the improved interdigital structure transducer for sensors and damage detection device for coating film with a large area are presented and optimized respectively. Meanwhile, the damage location algorithm based on time difference measurement and kernel density estimation algorithm is also optimized. The multiple damage detection is realized by a device with a 4 × 8 electrode array, and the relative error of damage area with 1 mm × 1 mm is less than 5%, and the lower detection limits of damage size are 0.3 mm × 0.3 mm.

Damage Identification and Localization in an Aluminium Plate Based on Lamb Waves

The article presents a method for identifying and locating damages in an aluminum plate using Lamb waves. For this, a network of PZT wafer type actuators/sensors is used to generate and detect Lamb waves. The damage location algorithm is sketched taking into account the network configuration of PZT sensors. The experimental results confirm the calculation procedure.

Lamb wave-based damage localization and quantification algorithms for CFRP composite structures

Accurately and quantitatively monitoring the damages in complicated composite structures is very challenging, but essential for assessing their integrity. In this paper, an innovative damage monitoring method based on Lamb wave is proposed for large-scale complicated composite laminates. A combination of a resource-constraint Levenberg-Marquardt algorithm associated with a quantum-inspired gravitational search algorithm is introduced to identify the damage location. A damage contour algorithm using convex envelope of damage reflection points and maximum inscribed n-polygon is developed to determine the quantitative damage size. Meanwhile, a singular loci removal scheme based on an automatically updated boundary region constraint and bivariate normal distribution is utilized to separate the correctly positioned points on the damage periphery from those associated with noise. Experiments on two large-scale composite panels with multiple damage cases are conducted to substantiate the proposed algorithms. Sensor layers with embedded piezoelectric transducer (PZT) sensor networks mounted on the structures are employed to excite and receive Lamb wave signals because of their superiority of flexibility, electrical stability and apt for complex structures. The results demonstrate that the proposed technique is capable of pinpointing both location and size of the damage in the complicated composite structures.

Lamb wave-based real-time damage location system with integrated internet of things

In this paper, an automated Structural Health Monitoring (SHM) system that uses Lamb waves is presented. A miniaturized prototype named ‘Real-Time Damage Location System’ (RTDLS) is designed and fabricated for thin plate structure, which can automatically locate single damage in an area of 100 mm × 100 mm by manually pressing a tactile button or by using an Internet of Things (IoT) platform. RTDLS is a lightweight, mobile, and plug-and-play device that uses 3 piezoelectric wafer transducers. It uses the pitch-catch technique with a novel damage location algorithm to locate damage in a plate. It displays the location of the identified damage on an LCD screen and on the webpage through live broadcasts using an IoT platform. RTDLS can store Lamb wave data corresponding to the pristine and damaged states of the structure in a FLASH memory so that no data is erased until reset action is performed.

Damage localization in pressure vessel using guided wave-based techniques: Optimizing the sensor array configuration to mitigate nozzle effects

Ultrasonic guided waves are desirable technologies for damage monitoring in large structures such as pipes and pressure vessels since they could propagate over a long distance with low attenuation. However, due to the complex scattering of guided waves, it is a great challenge to extract the damage information when using guided wave-based techniques to monitor the pressure vessels equipped with accessories. As an attempt, we investigated the interactive behavior between guided waves and pressure vessel nozzles. Ensemble empirical mode decomposition, fast Fourier transform and ellipse-based damage localization algorithm are adopted to perform the signal processing and damage localization analysis, respectively. A sensor array configuration optimization method is presented to mitigate the nozzle effects on the propagation of guided waves. The optimal number of sensors is determined experimentally, and the sensor array placement is optimized by a Particle Swarm Optimization algorithm. The accurate experimental damage localization results in the pressure vessel and curved plate indicate the effectiveness of our methods.

Damage Localization in Composite Plates Using Wavelet Transform and 2-D Convolutional Neural Networks.

Nondestructive evaluation of carbon fiber reinforced material structures has received special attention in the last decades. Usage of Ultrasonic Guided Waves (UGW), particularly Lamb waves, has become one of the most popular techniques for damage location, due to their sensitivity to defects, large range of inspection, and good propagation in several material types. However, extracting meaningful physical features from the response signals is challenging due to several factors, such as the multimodal nature of UGW, boundary conditions and the geometric shape of the structure, possible material anisotropies, and their environmental dependency. Neural networks (NN) are becoming a practical and accurate approach to analyzing the acquired data using data-driven methods. In this paper, a Convolutional-Neural-Network (CNN) is proposed to predict the distance-to-damage values from the signals corresponding to a transmitter-receiver path of transducers. The NN input is a 2D image (time-frequency) obtained as the Wavelet transform of the acquired experimental signals. The distances obtained with the NN are the input of a novel damage location algorithm which outputs a bidimensional image of the structure’s surface showing the estimated damage locations with a deviation of the actual position lower than 15 mm.

Impact damage localization and mode identification of CFRPs panels using an electric resistance change method

Electric resistance change method (ERCM) has shown potential as a good candidate for performing in-situ damage sensing in carbon fiber reinforced plastic composites (CFRPs), however, studies that investigated the impact damage localization and mode identification of CFRPs were limited. This paper investigates a novel ERCM technique for damage localization and mode identification in CFRPs under normal impact load with energies of 50 and 80 J. The impact damage characteristics of the CFRPs were firstly analyzed, and the corresponding electrical resistance was measured. A damage localization algorithm was designed according to the resistance characteristics obtained by the theoretical calculation and simulation. A relationship between CFRPs thickness and the electrical resistance was built, and damage modes were analyzed with assistance of computed tomography observations. Results show that the electric resistance changes between electrode networks could be used to help imaging the damage positions with a very high accuracy (6% error). The linear relationship between the electrical resistance, incident energy, and the panel thickness could be adopted to predict the damage modes in CFRPs after impact.

Upcoming damage size quantification in aeronautic composite structures based on imaging results post-processing

In this paper, a damage quantification strategy relying on post-processing of Lamb wave based damage localization results is presented. This method is able to predict the upcoming sizes of a delamination after a training step. Inputs of the proposed method are localization index maps produced by damage localization algorithms and representing the presence likelihood of a damage over the structure under study. The area covered by a high localization index around the estimated damage location are then extracted from these spatial probability maps. A data-driven model representing the mathematical relationship between this quantification feature and the actual size of the damage is finally inferred and used to predict future damage size. The proposed method is successfully validated on experimental data coming from CFRP plate samples equipped with piezoelectric transducers. Delaminations induced by fatigue testing and laser shock are studied. The sensitivity of the method to input frequency and damage localization algorithms parameters is assessed and a method to automatically select its own parameters is proposed. Furthermore, it is demonstrated that a model can be confidently learned on a given CFRP plate sample and transferred to predict damage size on another similar CFRP plate sample.

복합재 패널에서 유도 탄성파를 이용한 이미지 기반 손상탐지 기법 개발 : Part Ⅰ. 손상위치 탐지 알고리즘

본 논문은 복합재 패널에서 압전 작동기를 사용하여 탄성파를 생성하고, 손상에서의 반사된 신호를 압전 감지기에서 탐지하여 손상위치를 추정할 수 있는 알고리즘을 개발하였다. 손상이 없는 신호와 손상이 있는 신호를 비교하여 손상신호를 추정하는 진단적 접근방법을 사용하였다. 신호 상관관계를 이용하여 탄성파의 군속도를 계산하고 압전기 위치정보를 이용하여 손상정보를 추출하였다. 하지만 탄성파의 비선형 특성으로 인해, 손상정보는 다양한 신호의 조합으로 구성되기 때문에, 손상위치를 명확히 구별하기 어렵다. 이에 본 논문에서는 손상에서 반사된 신호정보를 신호 도달거리의 면적으로 변환해서 손상의 중심위치를 찾는 누적함수 특성벡터 알고리즘(CSFV, cumulative summation feature vector)을 새롭게 제안하고, 특성벡터를 손상지수와의 곱으로 표현하는 가시화 기법을 적용하였다. 또한 복합재 패널에서 실험검증을 수행하고, 기존의 알고리즘과의 비교를 통해 제안된 알고리즘이 정확도 높게 손상위치를 검출할 수 있음을 보였다.

Vibration-based method for story-level damage detection of the reinforced concrete structure

This study aimed to develop a method for the determination of the damaged story in reinforced concrete (RC) structure with ambient vibrations, based on modified jerk energy methodology. The damage was taken as a localized reduction in the stiffness of the structural member. For loading, random white noise excitation was used, and dynamic responses from the finite element model (FEM) of 4 story RC shear frame were extracted at nodal points. The data thus obtained from the structure was used in the damage detection and localization algorithm. In the structure, two damage configurations have been introduced. In the first configuration, damage to the structure was artificially caused by a local reduction in the modulus of elasticity. In the second configuration, the damage was caused, using the Elcentro1940 and Kashmir2005 earthquakes in real-time history. The damage was successfully detected if the frequency drop was greater than 5% and the mode shape correlation remained less than 0.8. The results of the damage were also compared to the performance criteria developed in the Seismostruct software. It is demonstrated that the proposed algorithm has effectively detected the existence of the damage and can locate the damaged story for multiple damage scenarios in the RC structure.

Damage detection & localization on composite patch repair under different environmental effects

This paper presents a structural health monitoring (SHM) methodology for detecting damage in a composite bonded repair. The application of guided wave based techniques in a step-sanded bonded repair under operational and environmental load is thoroughly investigated. A two step damage detection and localization algorithm is presented, were in the first level the path damage indices (PDIs) for each transducer pair is calculated. The PDIs are then compared to a set threshold (based on the environmental and operational conditions) to increase the reliability of damage detection while reducing false alarm. In addition, a self-diagnosis approach based on electro-mechanical impedance (EMI) measure is proposed to identify the faulty sensors prior to the diagnosis. Once the transducer pairs with possible damage in their path has been selected, the second level of the proposed methodology is damage localization. To address the challenge of edge reflection, complex geometrical shape and layup of the repair patch which introduced anisotropy to the wave propagation, a novel damage detection based on probability imaging technique is proposed. The methodology is developed based on assigning probabilities of damage to the Minimal Intersection Score (MIS) to reduce the path saturation related to each path having the same probability of damage being located anywhere along it. The proposed method, uses a smart sub-division technique based on Voronoi Tessellation which is adaptable to any shape (circular, rectangular, elliptical). The reliability of the proposed method is then demonstrated with experimental results on a composite step-sanded repair subjected to impact damage under vibration and temperature variations, and the choice of input parameters such as wave form and excitation frequency on the probability of detecting damage is demonstrated.

Damage localization in cross-ply laminated composite plates under varying temperature conditions using Lamb waves

Lamb wave-methods are reported to be very effective in detecting and localizing defects in thin plate structures. However, the accuracy of damage detection using Lamb waves is affected significantly by variations of environmental and operating conditions. In the present study, an improved methodology is presented to calculate the damage residue measure from current state responses that compensates the effect of operational and environmental changes. The proposed damage residue is obtained by comparing the current state responses of identical paths with the same propagation distance and direction. For damage localization in the structure, a modified delay-sum damage localization algorithm is introduced. The proposed damage residue measure is used along with the modified delay-sum algorithm to locate damage in a composite plate. The methodology is first presented through numerical simulations based on a finite element model and validated through experiments on a carbon fiber cross-ply configured composite plate. The insensitiveness of the proposed residue measure to temperature changes is demonstrated experimentally.

Vibration-based damage indicators: a comparison based on information entropy

Vibration-based methods for damage localizations often rely on a damage feature defined in terms of changes of modal shapes and localize damage by detecting shape irregularities that are ascribed to local loss of stiffness. In this paper, the performance of several algorithms for damage localization is investigated and the results are compared in terms of the gain of information they provide, which is measured by the relative information entropy also known as Kullback–Leibner (KL) divergence. This parameter is a measure of the difference between two probability distributions and can quantify the information gain achieved using different statistical models. In this paper the relative entropy is used to compare the gain of information obtained using different damage-sensitive indicators retrieved from simulated structural health monitoring data. The investigation is carried out using structural responses simulated using the finite element model of a real bridge permanently monitored by the Italian Seismic Observatory of Structures.