Damage Pattern Recognition Research Articles

Research on Acid Aging and Damage Pattern Recognition of Glass Fiber-Reinforced Plastic Oil and Gas Gathering Pipelines Based on Acoustic Emission.

Pipelines extend thousands of kilometers to transport and distribute oil and gas. Given the challenges often faced with corrosion, fatigue, and other issues in steel pipes, the demand for glass fiber-reinforced plastic (GFRP) pipes is increasing in oil and gas gathering and transmission systems. However, the medium that is transported through these pipelines contains multiple acid gases such as CO2 and H2S, as well as ions including Cl-, Ca2+, Mg2+, SO42-, CO32-, and HCO3-. These substances can cause a series of problems, such as aging, debonding, delamination, and fracture. In this study, a series of aging damage experiments were conducted on V-shaped defect GFRP pipes with depths of 2 mm and 5 mm. The aging and failure of GFRP were studied under the combined effects of external force and acidic solution using acoustic emission (AE) techniques. It was found that the acidic aging solution promoted matrix damage, fiber/matrix desorption, and delamination damage in GFRP pipes over a short period. However, the overall aging effect was relatively weak. Based on the experimental data, the SSA-LSSVM algorithm was proposed and applied to the damage pattern recognition of GFRP. An average recognition rate of up to 90% was achieved, indicating that this method is highly suitable for analyzing AE signals related to GFRP damage.

An adaptive convolutional neural network based on transmissibility grayscale image for online identification of offshore platform damage pattern

In response to the challenges in offshore platform damage pattern recognition, where traditional methods lack adaptability owing to relying on feature extraction and expert knowledge, a novel method for online recognition of offshore platform damage patterns using an improved convolutional neural network (CNN) based on transmissibility grayscale image is proposed. The original transmissibility function is converted into a two-dimensional grayscale image to capture the essential features in the original signal, which is then used as the input for the deep learning model. Subsequently, an attention mechanism is introduced to enable the CNN to focus on key areas of the image, emphasize important feature channels, dynamically adjust feature representations, and enhance its overall performance. Meanwhile, the model is optimized by virtue of variational Bayesian algorithm and early stopping criteria, and finally, the trained offline model is applied to online damage pattern recognition of offshore platforms. Through on indoor vibration monitoring experiments, seven different damage patterns of the platform model are recognized accurately, which show that the accuracy of the improved CNN model is increased by an average of 8.5 % as compared to traditional intelligent methods. Furthermore, the proposed method is applied to the online damage identification of offshore platforms in the field, and the corresponding prediction accuracy reaches 98.9 %, demonstrating the feasibility and generalizability of the proposed method in adaptive feature extraction and precise identification of damage patterns.

Analysis of crumb rubber content influence on damage evolution and pattern recognition of rubberised epoxy asphalt mixture using acoustic emission techniques

ABSTRACT The application of coarse crumb rubber (CR) to toughen the epoxy asphalt mixture has emerged as a viable choice. However, the evolution rule of strength and damage modes of mixtures with different crumb rubber contents remains elusive. The objective of this study is to clarify the failure mechanisms in rubberized epoxy asphalt mixture combined acoustic emission (AE) data and k-means clustering theory. Four types of epoxy asphalt mixture samples are tested with the monitor of AE sensors. Based on the temporal and frequential features of AE signals, damage evolution is evaluated by the Weibull function, b-value, and the relationship between the raise angle and average frequency at the peak loading. Moreover, the k-means algorithm is conducted to cluster the AE events and damage characteristics after pre-processing the data. The test results indicate that CR particles enhance the toughness of epoxy asphalt mixtures and the mixtures with fewer CR are susceptible to occur sudden fracture due to brittle coarse aggregates. The k-means method effectively identifies AE events into four types of damaged behaviours. The findings offer valuable insights into the strength attenuation, modification characteristics, damage evolution and damage modes of rubberized epoxy asphalt mixtures, contributing to the development of more durable and resilient asphalt materials.

An acoustic emission based approach for damage pattern recognition in composite using linear discriminant analysis

With the increasing application of composite components in various industries, the assessment of their structural integrity, the analysis of damage processes, and the identification of damage patterns are becoming increasingly important. The accuracy of the analysis relies heavily on the selection of features. This paper presents a new idea to extract effective damage features from acoustic emission (AE) signals and accurately identify different damages in the failure process of carbon fiber reinforced polymer specimens. The method combines Hilbert–Huang transform (HHT) and Linear Discriminant Analysis (LDA) to analyze the AE signals generated during the damage process of composite specimens. Specifically, the Hilbert marginal energy spectrum of the signals was regarded as frequency domain descriptors. The frequency domain descriptors were subsequently associated with the parametric features after dimensionality reduction by LDA to construct the classification framework named HHT-LDA. The results show that the frequency domain descriptors of the AE signals associated with each damage mode characterized distinctly. The frequency band of energy distribution in the raw waveform for matrix cracking, delamination, and fiber breakage are (100–150 kHz), (150–300 kHz), and (300–350 kHz), respectively. In addition, the three damage patterns mentioned above were successfully detected and recognized from the complex AE waveforms using HHT-LDA with 85% overall classification rate. This research idea will serve as a potential method for future composite damage pattern recognition and provide supporting knowledge for practical applications of AE monitoring.

Assessment of tensile damage mechanism of open‐hole GLARE laminates based on acoustic emission and digital image correlation techniques

AbstractGLARE laminates have emerged as a favored option for the construction of fuselage and wing skins in large airliners, owing to their exceptional mechanical characteristics. Nevertheless, the incorporation of open‐hole designs poses a challenge as it disrupts the continuity of the laminates, resulting in stress concentration and subsequent damage. To investigate the impact of various layup orientations and hole sizes on the tensile properties of open‐hole GLARE laminates, this study conducted axial tensile tests. Additionally, the tensile damage process was monitored using DIC and AE techniques, enabling the identification of damage patterns and the analysis of their evolution. The results demonstrate a noteworthy decline in the ultimate strength and failure strain as the size of the opening increases. Moreover, the retention rate of failure strain displays a marked sensitivity to the layup orientation. In conjunction with observations made through DIC and SEM, the k‐means++ algorithm successfully clustered peak frequencies, thereby revealing distinct damage patterns and their corresponding frequency ranges as aluminum alloy damage [0–90 kHz], matrix cracking [104–174 kHz], fiber/matrix debonding [175–224 kHz], interlaminar delamination [234–300 kHz], and fiber fracture [304–469 kHz]. AE cumulative counts were utilized to evaluate the progression of individual damage modes. The results emphasize that matrix cracking demonstrates the most substantial cumulative counts, whereas damage to the fibers and aluminum alloy noticeably affects the load‐carrying capability of the laminate. Furthermore, the fibers/matrix debonding and interlaminar delamination, exhibit heightened susceptibility to layup orientation and hole size.Highlights The tensile performance evaluation of GLARE laminates with open‐hole varying layup orientations and hole sizes was investigated. A combination of DIC and AE techniques was used to monitor the tensile damage process. Damage pattern was identified based on the observations of DIC and SEM. An approach based on Pearson's correlation coefficient and k‐means++ clustering algorithm was used for damage pattern recognition in GLARE laminates. AE cumulative counts were employed to assess the evolution of each damage mode.

Neural networks and damage pattern recognition in power transformer diagnostics

The results of detecting deformations and damage of power transformer windings using the transformer Frequency Response Analysis (SFRA) are presented taking into account RG CIGRE A2.26, Standard IEC 60076-18, Standard IEEE C57.149. The technology of pattern recognition by signal images for diagnostics of winding damage, type of defect, its localization is implemented. Neural networks are used - where in a multilayer perceptron with backpropagation of error - the work of neurons in a hierarchical network is imitated. Parametric methods - such as the Naive Bayes classifier - a probabilistic classifier based on the Bayes formula with the assumption of independence of features among themselves for a given class, which greatly simplifies the classification task due to the assessment of one-dimensional probability densities instead of one multidimensional. An algorithm for recognizing patterns of defects and damage to power transformers using the results of diagnostics by the FRA method has been developed.

Damage pattern recognition for corroded beams strengthened by CFRP anchorage system based on acoustic emission techniques

This study presents a damage pattern recognition approach for corroded steel beams strengthened by CFRP anchorage system based on acoustic emission clustering analysis. The proposed method includes four steps: acoustic emission signal acquisition, feature extraction, clustering analysis, and damage pattern recognition. Four corroded beams with different corrosion levels and strengthening schemes were tested under four-point bending loading. The acoustic emission signals were collected during the loading process and analyzed using Gaussian mixture model clustering method. The results showed that the collected AE data were analyzed using clustering analysis, successfully distinguishing the distinct damage patterns associated with each mode. The AE signals exhibited distinct characteristics for different damage modes: concrete matrix damage had high-frequency and low-energy characteristics, CFRP-matrix debonding showed intermediate values for all parameters, and CFRP tearing had longer durations, lower peak frequencies, and high-energy characteristics. Besides, the study identified three stages of the damage process: an initial stage with fewer low-intensity AE signals, a damage development stage characterized by an increase in concrete-matrix damage and CFRP – matrix debonding signals, and a continuous damage growth stage with significant AE signals associated with three damage modes. Furthermore, the degree of corrosion significantly influenced the cumulative AE energy of damage modes. Lower degrees of corrosion led to higher cumulative energy from concrete matrix damage and CFRP-matrix debonding. These findings provide valuable insights for understanding the damage evolution and failure mechanisms of CFRP-strengthened corroded beams. The use of AE techniques for damage pattern recognition can enhance the evaluation and design of CFRP anchorage systems, leading to more effective rehabilitation strategies for corroded structures.

Cluster analysis of acoustic emission signals for the damage pattern recognition of polymer concrete

Polymer concrete (PC) is a composite material that consists of mineral fillers (gravel, sand) and a polymer binder whose role is to replace the cement hydrate binders. Its properties such as mechanical strength, adhesion, etc. are improved to a great extent by polymer replacement. In this study, acoustic emission (AE) is used as a nondestructive tool to monitor the mechanical behavior of various PC samples subjected to three-point bending. In order to identify the acoustic signatures related to micro-cracks created within the polymer matrix and the different interfaces, experiments were first performed on manufactured dual-phase PC samples. Parameters related to the created micro-structural changes were then extracted based on different physical and signal processing considerations before being clustered using the time and frequency characteristics of the collected signals. After having carried out the same clustering approach on the three-phase PC (epoxy, sand, gravel), the present work shows that a new clustering of the AE signals is possible based on the wavelets of the AE hits processed as images. High classification accuracy was then obtained using the time–frequency characteristics of AE hits and a transfer learning technique.

Cluster analysis on damage pattern recognition in carbon/epoxy composites using acoustic emission wavelet packet

The purpose of this paper is to investigate the damage pattern recognition in carbon/epoxy composite laminates by Acoustic Emission technique. Three specimens with layup of [90/0/90/0]s were fabricated and subjected to three-point bending test. The dataset was clustered and analyzed by three unsupervised algorithms, namely, k-means, Self-Organizing Map, and Fuzzy C-Means. The results show that the signals can be divided into four clusters, which correspond to matrix cracking, delamination, fiber/matrix debonding, and fiber breakage, respectively. A method of energy feature extraction based on wavelet packet decomposition was used to analyze the clustering center to obtain the frequency band with relatively high energy corresponding each damage mode further determined the actual damage pattern of these signals with disputed classification results. The results of k-means illustrated that little difference in the determination of matrix cracking, preferring to assign more signal to inter-laminar damage and higher peak frequency to fiber breakage compared to the other two algorithms. It can verify the clustering results well and k-means algorithm performs better in damage pattern recognition of composites.

Enhancing anti-tumour innate immunity by targeting the DNA damage response and pattern recognition receptors in combination with radiotherapy.

Radiotherapy is one of the most effective and frequently used treatments for a wide range of cancers. In addition to its direct anti-cancer cytotoxic effects, ionising radiation can augment the anti-tumour immune response by triggering pro-inflammatory signals, DNA damage-induced immunogenic cell death and innate immune activation. Anti-tumour innate immunity can result from recruitment and stimulation of dendritic cells (DCs) which leads to tumour-specific adaptive T-cell priming and immunostimulatory cell infiltration. Conversely, radiotherapy can also induce immunosuppressive and anti-inflammatory mediators that can confer radioresistance. Targeting the DNA damage response (DDR) concomitantly with radiotherapy is an attractive strategy for overcoming radioresistance, both by enhancing the radiosensitivity of tumour relative to normal tissues, and tipping the scales in favour of an immunostimulatory tumour microenvironment. This two-pronged approach exploits genomic instability to circumvent immune evasion, targeting both hallmarks of cancer. In this review, we describe targetable DDR proteins (PARP (poly[ADP-ribose] polymerase); ATM/ATR (ataxia–telangiectasia mutated and Rad3-related), DNA-PKcs (DNA-dependent protein kinase, catalytic subunit) and Wee1 (Wee1-like protein kinase) and their potential intersections with druggable immunomodulatory signalling pathways, including nucleic acid-sensing mechanisms (Toll-like receptors (TLR); cyclic GMP–AMP synthase (cGAS)–stimulator of interferon genes (STING) and retinoic acid-inducible gene-I (RIG-I)-like receptors), and how these might be exploited to enhance radiation therapy. We summarise current preclinical advances, recent and ongoing clinical trials and the challenges of therapeutic combinations with existing treatments such as immune checkpoint inhibitors.

Self‐adaptive real‐time clustering analysis and damage pattern recognition for steel tube confined reinforced concrete structures through acoustic emission signals

Self‐adaptive real‐time clustering analysis and damage pattern recognition for steel tube confined reinforced concrete structures through acoustic emission signals

Evolutionary Insight into Immunothrombosis as a Healing Mechanism.

Both invertebrates and vertebrates possess a cluster of immediate and local wound-sealing, pathogen-killing, and tissue healing responses known as immunoclotting and immunothrombosis, respectively, to cope with two life-threatening emergencies, namely, bleeding and microbial invasion. Despite their convergence in function, immunoclotting and immunothrombosis are deployed by different blood cells and intravascular multidomain proteins. In vertebrates, these proteins share some domains with intrinsic chemical affinities useful in generating cooperative networks such as pathogen and damage pattern recognition molecules. Moreover, many of the proteins involved in coagulation and fibrinolysis in humans are multifunctional molecules playing roles in other processes from inflammation to healing and beyond. In our modern society, however, the interaction of activated intravascular allosteric proteins with one another and with blood cells entails vulnerabilities posing a biological paradox: intravascular proteins that locally operate as tissue repair enhancers can nevertheless generate pathogenic processes by acting systemically. In this manuscript, we contextualize and frame the coagulation system and hemostasis through an evolutionary time scale, illustrating their role as dual players in the defense against exsanguination and pathogens while significantly influencing wound healing.

Damage Pattern Recognition and Crack Propagation Prediction for Crumb Rubber Concrete Based on Acoustic Emission Techniques

In this study, the clustering method of the concrete matrix rupture and rubber fracture damages as well as the prediction of the ultimate load of crumb rubber concrete using the acoustic emission (AE) technique were investigated. The loading environment of the specimens was a four-point bending load. Six clustering methods including k-means, fuzzy c-means (FCM), self-organizing mapping (SOM), Gaussian mixture model (GMM), hierarchical model, and density peak clustering method were analyzed; the results illustrated that the density peak clustering has the best performance. Next, the optimal clustering algorithm was used to cluster AE signals so as to study the evolution behavior of different damage modes, and the ultimate load of crumb rubber concrete was predicted by an artificial neural network. The results indicated that the combination of AE techniques and appropriate clustering methods such as the density peak clustering method and the artificial neural network could be used as a practical tool for structural health monitoring of crumb rubber concrete.

Auto-Regressive Integrated Moving-Average Machine Learning for Damage Identification of Steel Frames

Auto-regressive (AR) time series (TS) models are useful for structural damage detection in vibration-based structural health monitoring (SHM). However, certain limitations, e.g., non-stationarity and subjective feature selection, have reduced its wide-spread use. With increasing trends in machine learning (ML) technologies, automated structural damage recognition is becoming popular and attracting many researchers. In this paper, we combined TS modeling and ML classification to automatically extract damage features and overcome the limitation of non-stationarity. We propose a two-stage framework, namely auto-regressive integrated moving-average machine learning (ARIMA-ML) with modules for pre-processing, model parameter determination, feature extraction, and classification. Based on shaking table tests of a space steel frame, floor acceleration data were collected and labeled according to experimental observations and records. Subsequently, we designed three damage classification tasks for: (1) global damage detection, (2) local damage detection, and (3) local damage pattern recognition. The results from these three tasks indicated the robustness and accuracy of the proposed framework where 97%, 98%, and 80% average segment accuracy were achieved, respectively. The confusion matrix results showed the unbiased model performance even under an imbalanced-class distribution. In summary, the presented study revealed the high potential of the proposed ARIMA-ML framework in vibration-based SHM.

Dynamic feature evaluation on streaming acoustic emission data for adhesively bonded joints for composite wind turbine blade

Damage mode identification and premature failure prevention for composite structures by acoustic emission have drawn a great deal of attention. Feature evaluation on streaming acoustic emission data is one of the significant issues in research of acoustic emission signal processing. This work conducts dynamic feature evaluation on 15 conventional acoustic emission features so as to seek a deeper insight into different features with damage accumulation. First, the procedure of dynamic feature evaluation is presented based on three basic algorithms. Second, the streaming acoustic emission data are collected from the adhesively bonded composite single-lap joint subjected to quasi-static tensile loads. Third, further efforts are made so as to explore the information contained as well as to interpret the effect of damage accumulation. It is found that different conventional acoustic emission features show distinctive functions, including damage mode identification, damage process indication, and both of them. Informative features for damage pattern recognition are independent on damage accumulation. Useful features for damage process description show sensitive dynamic characteristics with damage accumulation, especially before the complete failure of the specimen. Furthermore, dynamic feature evaluation can be used to detect singular signals.

Cluster analysis and damage identification for FRP/steel-confined RC column using AE technique

This article presents the damage evaluation and pattern recognition for the newly proposed fiber reinforced polymer (FRP)/steel-confined reinforced concrete columns. The interaction of FRP material, steel tube, and reinforced concrete lead to complex damage mechanisms and invisible damage modes. The prevailing acoustic emission (AE) technique was applied to monitor the damage process and detect the sheltered damages under cyclic loading. Characteristic AE parameters, such as energy and duration, were extracted to disclose the damage evolution and evaluate the damage state. Three typical damage stages were identified. The fuzzy C.means (FCM) algorithm and particle swarm optimization (PSO) algorithm were applied as efficient clustering tools to discriminate different damage signals of FRP/steel-confined RC columns. Five types of damage mechanisms were identified and illustrated based on the statistical analysis of typical AE features. Furthermore, typical damage waveforms were extracted, the frequency content of each damage signal was discussed on the basis of wavelet transform.

Research on the application of clustering and classification in battlefield damage identification

This paper explores the solutions to the problem of battlefield damage pattern recognition. The main research results of the two components of clustering and classification are discussed respectively, and the main characteristics and existing problems of the existing methods are analyzed. The advantages, disadvantages and application scope of different methods are described in detail, which provides a basic theoretical guidance for the study of battlefield damage identification.

Comparison on mechanical properties, damage evolution and aging effects of multi-delaminated composites under three point bending

The study on natural aging of fiber reinforced composite, delamination damage behavior and damage pattern recognition plays a significant role in structural health monitoring of the material. In this study, acoustic emission is used to investigate damage evolution process and damage mechanisms in unidirectional glass and carbon-glass orthogonal woven fiber reinforced composites containing symmetric multiple delaminations under three point bending testing. Based on the principle of control variables, four kinds of specimens are manufactured to investigate the comparative study for the effects of delaminations, reinforced materials and fiber orientation on buckling failure behaviors and the effects of aging on mechanical properties. The results indicate that the degradation of strength and stiffness of composite can be influenced by the existence of delamination defects and natural aging condition. Both negative and positive effects can be generated by natural aging condition. Moreover, reinforced materials, porosity and fiber orientation are important factors influencing the reliability of composite. Clustering results of unidirectional fiber reinforced composites indicate interphase failure is the main damage component, moreover, the existence of symmetrical multiple delaminations will aggravate the damage of composite and make instability failure characterized by intermittency.

A numerical study on multi‐site damage identification: A data‐driven method via constrained independent component analysis

This paper presents a solution to the multi-site structural damage identification problem using a data-driven method and constrained independent component analysis (cICA). While existing studies in this field presented encouraging results for single-site damage identification, limited research effort has been devoted to identifying multi-site damage due its complexity. Efficient features for single-site damage identification may lose their effectiveness when multi-site damage occurs. This paper extracts damage-sensitive features from the ICA outcome of the structural responses under certain excitations. The information on structural damage contained in the response is compacted into the mixing matrix by enforcing identical independent components to that of intact structures. Hence, the cICA can significantly reduce the feature dimension and preserve all the valuable information of damage. A case study indicates that the mixing matrix elements, when used as damage features, can distinguish multi-site damage cases from single-site damage cases and locate the single-site damage. Furthermore, the mixing matrix columns of multi-site damage cases exhibit distinct correlation with that of the corresponding single-site damage cases. As a result, the proposed method can progressively locate the structural damage. Moreover, the present study has the potential to identify multi-site damage identification in data-driven structural health monitoring without requiring multi-site damage data as a reference. This relieves the burden from data incompleteness when using data-based damage identification methods and pattern recognition.

Damage pattern recognition and damage evolution analysis of unidirectional CFRP tendons under tensile loading using acoustic emission technology

The purpose of this paperis to use acoustic emission (AE) technology to realize the damage pattern recognition and the damage evolution of unidirectional (UD) CFRP tendons in the tensile process. In order to realize this goal, the tensile test of UD CFRP tendons was carried out. The adaptive Hilbert–Huang transform was used to analyze the AE signal in time–frequency domain to obtain the Hilbert marginal energy spectrum (HMES) and instantaneous energy spectrum (IES) of the signal. Damage pattern recognition was realized through HMES and IES. In this paper, a mutation function and a cumulative function were proposed based on HMES to study the evolution of different damage mechanisms and the internal relationship between them. In order to verify the reliability of the proposed method, digital image correlation (DIC) technique was used to obtain the strain distribution of the UD CFRP tendon specimen to determine the occurrence of local damage. The results of this investigation can be used to guide the establishment of the health monitoring mechanism for prestressed CFRP structures based on AE technology.