Structural Health Monitoring Approach Research Articles

Damage sensing in multi-functional nanocomposite polymer bonded energetics with embedded multi-walled carbon nanotube sensing networks

Abstract This experimental investigation evaluates the strain and damage sensing abilities of multi-walled carbon nanotube (MWCNT) networks embedded in the binder phase of polymer-bonded energetics (PBEs). PBEs are a special class of particulate composite materials that consist of energetic crystals bound by a polymer matrix, wherein the polymer matrix serves to maintain the composite’s shape and form. The structural health monitoring (SHM) approach presented in this work exploits the piezoresistive properties of the distributed MWCNT networks. Major challenges faced during such implementation include the low binder concentrations of PBEs, the presence of conductive/non-conductive particulate phases, the high degree of heterogeneity in the PBE microstructure, and achieving the optimal MWCNT dispersion. In this study, ammonium perchlorate (AP) crystals as the oxidizer, Aluminum grains as the metallic fuel, and Polydimethylsiloxane (PDMS) as the binder are used as the constituents for fabricating PBEs. To study the effect of each constituent on the MWCNT network’s SHM abilities, various materials systems are comprehensively studied: MWCNT/PDMS materials are first evaluated to study the binder’s electromechanical response, followed by AP/MWCNT/PDMS to assess the impact of AP addition, and finally, AP/AL/MWCNT/PDMS to evaluate the impact of adding conductive aluminum grains. Compression samples (ASTM D695) were fabricated and subjected to monotonic compression. Electrical resistance is recorded in conjunction with the mechanical test via an LCR meter. Gauge factors relating to the change in normalized resistance to applied strain are calculated to quantify the electromechanical response. MWCNT dispersions and mechanical failure modes are analyzed via scanning electron microscopy imaging of the fracture surfaces. Correlations between the electrical behavior in response to the mechanical behavior are presented, and possible mechanisms that influence the electromechanical behavior are discussed. The results presented herein demonstrate the successful ability of MWCNT networks as SHM sensors capable of real-time strain and damage assessment of PBEs.

Experimental validation of an hybrid analytical-numerical technique to estimate the directivity of ultrasonic piezoelectric actuators

The generation of guided waves is a common approach for structural health monitoring. The optimization of the excitation strategies very much benefits from the availability of efficient simulation tools. On the one hand, the application of finite elements (FEs) for the analysis of wave propagation problems leads to large models which tend to be computationally costly for optimization studies. On the other hand, semi-analytical approaches provide useful analytical expressions for parametric studies, but may lead to inaccurate estimations of tuning frequencies and directionality, especially at high frequencies. This is mostly because of the simplifying assumptions regarding the stress field at the interface between the actuator patches and the plate. An interesting methodology couples the analytical far-field solution of the plate response with a local FE model of the interface stresses between plate and patches. This method allows the analysis of complex actuation configurations for various purposes, such as directivity steering. In this contribution, we experimentally validate such hybrid FE-analytical technique. A Scanning Laser Doppler velocimeter is employed to retrieve both temporal and spatial Fourier Transforms on a polycarbonate plate, excited by piezoelectric patches, and the directivity patterns of the first symmetric and antisymmetric modes are compared to the numerical predictions.

Structural health monitoring of scarf bonded repaired glass/epoxy laminates interleaved with carbon non-woven veil

Bonded repair patches/joints often introduce vulnerabilities in composite laminates, making them prime candidates for structural health monitoring (SHM). In this study, stepped-scarf bonded joints were manufactured using glass fibre-reinforced epoxy laminates as representative repair patches, and a novel SHM approach through the electrical resistance change method was applied. To establish an electrically conductive path within the stepped-scarf joint, non-woven carbon fibre veils with areal weights of 10 g/m² and 20 g/m² were interlaid along the stepped bondline. Two types of tensile tests were performed. In the first set of tests, the stepped-scarf joints underwent monotonic quasi-static tensile loading until the bondline was completely fractured (catastrophic failure) and the change in electrical resistance was continuously monitored. The failure stress of the joint with a 10 g/ m² carbon veil was only marginally decreased (∼2 %) in comparison with that of the joints without a carbon veil, while the failure stress of the joint with a 20 g/m² carbon non-woven veil was considerably decreased (by ∼9 %). However, the joints with 10 g/m² and 20 g/m² carbon veils exhibited a significant change in electrical resistance (∼200 % and ∼1000 %, up to full failure, respectively). Simultaneously, the change in electrical resistance was used for the detection of damage initiation and progression, supported by digital images taken during the tests. In the second set of tests, the joints were subjected to a cyclic tensile loading/unloading regime and the change in electrical resistance was monitored. A significant amount of permanent change in resistance during the unloading phases (up to 120 % in the bondline with a 20 g/m² veil) was observed, providing insights into the laminate and bondline damage evolution. In addition, thermal images obtained with the joule heating method in the cyclic tensile tests were used to confirm the damage detected with the electrical resistance change method. Moreover, the micrographs from the fracture surfaces indicated that the variations in electrical resistance change are largely caused by damage occurring within or near the carbon veils. In conclusion, the results demonstrate that the presented SHM approach, which incorporates carbon non-woven fibre veils within non-conductive laminate composites, holds promise for monitoring damage initiation and propagation in repaired composite laminates as well as adhesively bonded composite laminate joints, without adversely influencing the structural integrity of the bondline.

Advancing structural health monitoring: Deep learning-enhanced quantitative analysis of damage in composite laminates using surface strain field

Composite materials have been widely used as critical components in aerospace applications due to their excellent performance characteristics. The real-time accurate identification and quantification of various types of damage within composite material structures pose a significant challenge. This study introduces an innovative damage detection method based on strain fields, which centrally employs deep learning techniques. Utilizing the Res-Mask R–CNN, this study accurately detects and categorizes various forms of damage within composite laminates, including open holes, subsurface holes, and delamination. Moreover, this method also enables precise localization and quantification of damaged areas. A series of experiments and simulations have validated the accuracy and robustness of the network model. Damage inversion experiments demonstrate that the area error of the damaged regions has been reduced to 7.4 %, and the positional error does not exceed 3.31 mm. In simulated scenarios, the shape context distance for complex damage contours does not exceed 0.21, indicating that the critical geometric features of the damage have been successfully preserved. This study provides an effective new approach for damage detection and real-time structural health monitoring of composite laminates.

Data-Driven Multi-Fault Detection in Pipelines Utilizing Frequency Response Function and Artificial Neural Networks

This research presents a data-driven structural health monitoring (SHM) approach for pipeline systems that leverages frequency response function (FRF) signals and artificial neural network (ANN) algorithms to accurately identify and classify diverse pipeline fault conditions. The study focuses on three specific faults: bolt looseness, scale deposits, and crack occurrence at pipeline supports, which were replicated on a pipeline segment located at the Sound and Vibration Research Group (SVRG) at University Putra Malaysia (UPM). The FRF signals were captured using accelerometers to monitor the structural health of the pipeline. The data acquisition stage involved collecting FRF signals from the accelerometers to capture vibrations and responses related to the identified faults using a Siemens LMS SCADAS data acquisition unit. The data underwent preprocessing, including the application of principal component analysis (PCA) for feature selection. The subsequent data processing stage involved the application of an artificial neural network (ANN) algorithm for pattern recognition to analyze and classify the acquired data, identifying patterns associated with the replicated fault conditions. The proposed methodology demonstrated exceptional performance, with the ANN model achieving consistently high overall accuracy (above 99.7%) and remarkably low mean squared error (in the range of 0.0088×10−3to0.3062×10−3) across multiple iterations and sensor datasets. The detailed class-specific metrics, including accuracy, precision, sensitivity, and F1-score, further substantiated the model's effectiveness in identifying the individual fault types with near-perfect or perfect results for the majority of the fault scenarios. The location-invariant performance of the ANN model across different sensor placements demonstrates the robustness of the proposed data-driven SHM methodology. This research highlights the transformative potential of integrating state-of-the-art data-driven techniques to revolutionize the monitoring and assessment of critical pipeline infrastructure, ultimately enhancing the safety, reliability, and longevity of these vital systems.

Off-the-shelf UHF RFID-based sensors for corrosion characterization of coated steel

The application of the UHF RFID technique in non-destructive testing (NDT) and structural health monitoring (SHM) has gained increasing attention due to its wireless, battery-less, and cost-effective attributes. It offers a promising approach for SHM and the Internet of Things (IoTs). This paper reports commercial off-the-shelf (COTS) flexible UHF RFID tag-based sensors for corrosion characterization on coated mild steel. Two types of COTS flexible UHF RFID tags with different bends are fabricated as sensors. The received signal strength indicator (RSSI) measurement is implemented to characterize the corrosion. Three parts (T-match area, dipole arms, and dipole loadings) of the two tags are tested for sensing purposes, and comparison and discussion of sensitivity, read range, and results are provided. This study successfully validates the feasibility of the proposed tag-bent method for corrosion characterization undercoating. It can be concluded that the dipole arm part of the applied COTS tags is the most sensitive area.

Assessing structural homogeneity and heterogeneity in offshore wind farms: A population-based structural health monitoring approach

This paper presents an in-depth analysis of population-based structural health monitoring applied to offshore wind farms, with a specific focus on a real wind farm. The study quantifies the heterogeneity within the wind turbine population utilising advanced numerical methods. The Fréchet distance is introduced as a key metric to assess the similarity in terms of structural integrity among the turbines considering factors, such as geometry, material properties, topology, and operational conditions. The results revealed a predominant homogeneity with respect across the wind turbines, despite variations in environmental exposure and operational states. Furthermore, the scope of the study is extended to explore the implications of environmental influences, such as corrosion, scour, and marine growth, which are typically unmonitored yet crucial for comprehensive structural assessment. The present paper contributes to the field by demonstrating the feasibility of knowledge transfer in homogeneous wind turbine populations and highlighting the need for detailed similarity assessments in heterogeneous settings. The findings of this study pave the way for better-targeted, smart, and efficient structural health monitoring strategies in renewable energy systems.

Transfer learning-based Gaussian process classification for lattice structure damage detection

This study presents a novel approach for real-time vision-based structural health monitoring, focusing on evaluating the deformation state of lattice structures. The structures are renowned for their remarkable recovery capabilities and exhibit similar mechanical responses under compressive loads. Despite these characteristics, quickly assessing the health status of the applied structure using knowledge from another related lattice structure is usually time-consuming and impractical. To address this, we propose to combine Gaussian process classification with transfer learning, termed TL-GPC, to detect damage states under compressive loads while also achieving uncertainty quantification. By employing structural deformations captured via the optical flow algorithm as inputs, the internal transfer kernel factor in TL-GPC is tailored to model knowledge transfer between source and target domain inputs. Experimental results show that the proposed TL-GPC model can deliver higher damage detection accuracy while ensuring stable uncertainty quantification in scenarios with limited and unbalanced experimental data.

Acoustic emission monitoring for damage diagnosis in composite laminates based on deep learning with attention mechanism

During service, advanced composite materials are susceptible to various forms of damage. Developing an efficient real-time Structural Health Monitoring (SHM) method is of profound significance to ensure the normal operational integrity of the advanced composite structures. This study introduces a novel end-to-end SHM approach employing acoustic emission (AE) techniques and a deep learning model with attention mechanisms (AM) to automate the damage diagnosis process and improve prediction accuracy. Low-velocity impact (LVI) experiments were carried out, recording AE signals for subsequent bi-spectrum analysis. Three dedicated AM-based Convolutional Neural Network (CNN) architectures were designed for damage diagnosis, utilizing 2D bi-spectrum contour images as input data. A comparison was made between the proposed CNN model, deep learning and traditional machine learning models in the context of damage diagnostics, revealing that CNN models combined AM exhibit significantly higher accuracy in pattern recognition tasks. The accuracies of the baseline CNN, self-attention-CNN, multi-head attention-CNN and convolutional block attention module-CNN are 98.22%, 98.50%, 98.85%, and 98.87%, respectively. The CNN embedded with AM demonstrated superior predictive performance and fewer instances of misclassification. Considering the specific demands of damage diagnostics in composite materials, the CBAM-CNN is better suited for composite material health monitoring tasks.

Predictive-Cognitive Maintenance for Advanced Integrated railway Management

Railway systems play a vital role in modern transportation and Predictive-Cognitive Maintenance (PCM) has emerged as a transformative approach in the context of Advanced Integrated Railway Management for ensuring the safety, reliability, and efficiency of these systems. PCM leverages data analytics and machine learning to optimize railway system maintenance. This requires effective structural health monitoring (SHM) using low-cost sensor devices. This paper presents a prototype solar-powered wireless sensor node with a 3-axis MEMS accelerometer and energy-harvesting features for monitoring rail track vibrations. The node contains a microcontroller that runs embedded machine-learning models to preprocess the vibration data after train crossing. Abnormal vibrations, indicative of defects, were detected in real time using the TinyML inference at the edge. Instead of raw data, only the model results were wirelessly transmitted to a digital twin in the cloud. The digital twin aggregates data across the rail network for system-level assessment of the RUL and maintenance planning. This edge computing approach minimizes wireless transmission and cloud storage compared to raw sensor streaming. Embedded ML enables real-time damage detection, whereas the cloud digital twin enables system-level prognosis insights. The solar-powered platform enables long-term remote monitoring at low cost without wiring or battery changes. A full-scale physical model was used to validate the edge node prototypes against calculation models and wired accelerometers for impulse loads. The results demonstrate that these nodes can provide a sensor layer for cost-effective PCM in railway systems. In summary, this work proposes an edge computing and embedded ML approach for SHM that integrates cloud-based digital twins to enable the predictive-cognitive maintenance of railway infrastructure. Wireless nodes demonstrate potential for low-cost, convenient, and automated rail health monitoring.

Guided Wave Behavior in Type IV Composite Overwrapped Pressure Vessels Under Fatigue Loading

Innovative Ultrasonic Guided Wave Approach for Structural Health Monitoring of Type IV Composite Overwrapped Pressure Vessels Seyedreza Hashemi, Jan Heimann, Amir Charmi, Eric Duffner, Jens Prager Abstract: The digitalization of quality control processes and the use of digital data infrastructures is a novel idea that can be applied for ensuring the operational safety and reliability of pressure vessels, particularly in the context of hydrogen storage at high pressure. Despite the critical role these pressure vessels play, current safety regulations lack an established concept for Structural Health Monitoring (SHM). This research addresses this gap by presenting a study on the application of ultrasonic guided waves (GWs) for SHM of Type IV Composite Overwrapped Pressure Vessels (COPVs). The study focuses on the development of a reliable measurement system to transition from conventional periodic inspections to SHM and predictive maintenance, prolonging the remaining lifetime of the vessels. A sensor network is employed, consisting of fifteen piezoelectric wafers arranged in three rings, which are mounted on the outer surface of the COPV. Deploying GWs, known for their long-distance propagation and ability to cover complex structures, the study explores GW behavior under different environmental and operational conditions, including periodic pressure fluctuations and temperature loadings. Meticulous analysis of GW signals by utilizing various features and damage indices, underscores their suitability for an effective SHM under realistic working conditions. The project aims to localize defects by considering temperature, and internal pressure. Mimicking the continuous monitoring of Type IV COPVs in H2 refueling gas stations under authentic operational conditions, the COPV underwent thousands of pressure load cycles in our special test facility . The implemented methodology facilitates early damage detection, showcasing the efficacy of the designed method in effective safety assurance.

Acoustic emission monitoring for industrial pressure vessels and infrastructure

Acoustic emission (AE) is a phenomenon that occurs in materials that are exposed to stress. Defects in the material, such as cracks can grow and release energy in the form of a mechanical wave that propagates through the material. With the help of piezoelectric sensors, the mechanical wave is converted into an electrical signal. The signals are recorded by the measuring system and are available for further processing. Acoustic emission as a method that records and processes the incoming signals in real time is therefore very suitable for 24/7 long-term structural health monitoring. With new approaches in data processing, acoustic emission can be moved from the classic periodic test approach to an online monitoring process. The in-service monitoring brings measuring challenges with it, like application on hot surfaces and inside explosion hazard areas. New developments in hardware and software provide tailor-made solutions for a variety of monitoring tasks. The use of artificial intelligence algorithms helps to improve the analysis of measurement data and guarantees safe operation of pressure equipment and infrastructure around-the-clock. Some examples of continuous monitoring are shown on pressure vessels from the industry sector and on infrastructure buildings e.g., steel bridges. Also, a new lean system approach in structural health monitoring with acoustic emission is presented.

“Why Should I Trust You?”: Exploring Interpretability in Machine Learning Approaches for Indirect SHM

Currently, machine learning (ML) methods are widely adopted in structural health monitoring (SHM), yet they are still mostly black boxes. On the other hand, given the significant responsibility associated with SHM, understanding the rationale behind it is critically important. In some cases, even experienced experts have difficulties finding evidence related to structural integrity within complex structural signals. Thus, solely relying on these black-box SHM systems carries inherent risks. Trustworthiness is the key for decision-makers when planning to act on predictions or deciding whether to deploy a new model. This understanding can also offer insights about the models, transforming untrustworthy models or predictions into reliable ones. The indirect SHM method using passing vehicles, an emerging technique in the past two decades, offers a rapid and cost-effective solution for bridge monitoring. Its signal components are affected by factors such as vehicle dynamics and road roughness, making them more complex than those in the direct method. Although ML methods have shown promising results in this domain, their results require further explanation. In this work, SHAP (SHapley Additive exPlanation) tools are utilized to interpret the result prediction of ML methods in indirect SHM. The trustworthiness of models is demonstrated through simulation databases: deciding whether a prediction should be trusted, choosing between models, and determining why a classifier should not be trusted.

Effect of Different Weight on the Movable PZT Device on the Damage Detection Performance of Electromechanical Impedance Technique

This study presents a novel approach to conducting the electromechanical impedance (EMI) technique for delamination detection in composite structures without the need for permanently attaching PZT (Lead Zirconate Titanate) transducers to the surface. Instead, a device is created that can be simply placed on top of the composite structure, enabling one to perform the EMI technique for detecting damage. The primary objective is to investigate the effectiveness of this device in detecting delamination within composite materials. Additionally, this study explores the impact of placing additional weight on top of the transducer to investigate the performance of the device subjected to higher pressure. Experimental results and analysis will be presented to evaluate the feasibility and reliability of this approach for non-destructive testing and structural health monitoring of composite components. This research is significant as it lays the groundwork for developing automated damage detection systems using robotics in the near future. By demonstrating the proposed concept that can be easily integrated into robotic platforms, this study contributes to the advancement of automation in structural health monitoring. Implementing this technique in robotic systems has the potential to revolutionize maintenance practices by enabling continuous, real-time monitoring of composite structures, enhancing safety, and minimizing downtime due to structural defects. Moreover, the investigation into the impact of additional weight on the transducer’s performance is crucial for setting minimum weight limits in robotic systems, ensuring optimal functionality and accuracy during automated damage detection tasks.

An unsupervised damage detection method for an operating wind turbine blade

Wind turbine blades are normally exposed to significant dynamic loads over their lifetime, including extreme conditions. This can lead to several types of damages such as cracking and delamination. Monitoring the structural condition of the blades to detect damages before they become catastrophic can be important. In recent years, the concept of data-driven structural health monitoring (SHM) approaches for wind turbine blade damage detection has become popular. These methods employ features collected from time series obtained from monitored wind turbine blades to identify damages. However, selecting an optimum number of features, which can reduce computational cost and minimize the effects of environmental and operational conditions, is still a challenge. In this paper, a data-driven approach is developed combining a feature extraction strategy and un-supervised anomaly detection. Monitoring data collected from several accelerometers mounted on a blade of an operating Vestas V27 wind turbine in healthy and damaged scenarios are used in this regard. Several time-domain, temporal, and time-frequency domain features are employed for the purpose of anomaly detection using unsupervised learning methods. Isolation Forest and One-class Support Vector Machine are used to train for anomaly detection. A threshold of detection is established to distinguish between the healthy and damaged states. Results show that the proposed approach can successfully identify the damage cases, but environmental and operational conditions still have some effect when it comes to identifying the extent of the damage.

Deep-Learning-based damage detection of an experimentally tested full-scale wind turbine blade.

Reducing the Operation and Maintenance (O&M) costs of Wind Farms can substantially contribute towards adopting environmentally sustainable and cost effective energy sources. O&M costs in Wind Turbines can account up to 50% of the Turbine’s whole life cycle, with the majority of it being the frequent manual inspection needed for Wind Turbine Blades (WTB). Vibration-based structural health monitoring (SHM) approaches can be used for detecting WTB damages in an early state, before they lead to catastrophic failure, while they can be used to perform inspection on an informed basis, reducing O&M costs. In the present study, a data-driven damage detection framework is developed for WTBs. A Finite Element Model (FEM) of a full-scale WTB (the Aventa AV-7 “Light Wind Turbine”, sold under the Leichtwindanlagen© name) is developed in ABAQUS FEA using sparce material properties and dimensions and subsequently modified to match a physical WTB. Damage is modelled in the form of a Trailing Edge (TE) longitudinal crack propagated in a stepwise manner. The FE model is updated based on the geometry altering and material property degrading effects of the TE crack. Numerical simulations are performed to obtain the dynamic responses of the blade, under white noise and chirp excitation. A damage detection framework is then developed using Auto-Regressive, with exogenous excitation (ARX) or without (AR), models and its Nonlinear counterparts NARX and NAR models. In addition, model dimensionality reduction is performed using Principal Component Analysis (PCA) and Deep-Autoencoders (D-AE). The results are presented in terms of Receiver Operating Characteristics (ROC) curves.

Physics-driven deep neural networks for damage identification using guided wave damage interaction coefficients

In this paper, recent advancements in the development of a guided wave-based damage identification approach using wave damage interaction coefficients (WDICs) and deep neural networks (DNNs) are presented. These WDICs uniquely describe the complex scattering of guided waves around possible damages and depend on the properties of the damage itself. Hence, they are utilized as physics-based and highly sensitive damage features herein. It is demonstrated, that DNNs can effectively learn intricate relationships between damage characteristics and complex-shaped WDIC patterns from a compact sized training dataset. In this study, two training datasets are created by numerical finite element simulations and experimental scanning laser Doppler vibrometer measurements using a pseudo-damage approach. Therefore, the orientation and thickness of surface-bonded artificial damages are varied to generate the training data of 12 selected damage scenarios. The generalization capabilities of the fully trained DNNs allow to accurately predict WDICs even for damage scenarios unseen during training. The presented damage identification method leverages this powerful ability to characterize properties of unknown damages. Once trained, the accurate DNN predictions become available promptly and can be compared with measured WDICs from an unknown damage for selected sensor positions. The performance of both simulation-based and experiment-based structural health monitoring approaches are assessed, while also addressing current limitations and possible enhancements. For example, the great potential of incorporating the phase information of the complex-valued WDICs in the identification procedure is discussed. Hence, this study highlights the latest advancements in the development of the presented damage identification method with promising results and provides valuable insights in the application of WDICs as physics-based features for DNNs.

Robotic-based terahertz spectroscopy imaging for non-destructive monitoring of water loss-induced damage in timber materials under low temperature stress

Timber materials are widely used in various industries, from construction to furniture manufacturing. One of the critical factors affecting the structural integrity and longevity of timber is water loss-induced damage, which often occurs under low-temperature stress conditions. Monitoring and assessing this damage traditionally involve invasive and time-consuming methods. This study explores the application of robotic-based terahertz spectroscopy 3D imaging as a non-destructive and efficient system for assessing the impact of water loss on timber materials under low-temperature stress. The detection and localization of damage in a various species of timber-based structures were experimentally investigated by performing pulsed terahertz time-domain spectroscopy and using a six- DOF (degree-of-freedom) robotic arm. The thickness and damage length were monitored by designing the reflection geometry of the terahertz scanner. Additionally, the terahertz source was controlled with a six-DOF robot arm for path planning. The corresponding measured dielectric property reference value for the damage identification was computed using a captured terahertz wave at 12% of moisture content. The reflected signals were analyzed to determine the thickness, defectiveness, and locations of the defects through signal processing in the time and frequency domains. Finally, the validity of the structural health monitoring approach for 3D detection and visualization of water loss-induced defects in timber structures using terahertz waves, was discussed.

Experimental Verification on Deep Learning based Monitoring Algorithms for Early Detection of Damage in Buried Pipelines

Recent increases in buried pipeline damage accidents due to third-party interference have significantly heightened attention towards buried pipeline monitoring. Especially, as the sudden damage can lead to large-scale leakage, there is a necessity for preemptive response and maintenance. However, the application of a structural health monitoring approach is difficult, since the extensive network of buried pipelines, stretching over thousands of kilometers, exhibits diverse noise environments and propagation characteristics. As a result, challenges within the buried pipeline system frequently lead to damages being overlooked. In this study, introduces a deep learning-based pipeline damage monitoring algorithm, specifically designed to early detection of accidents caused by third-party interference. This algorithm integrates a CNN-based anomaly detection model, advanced signal processing for data preprocessing, and TDoA-based source localization. The training and test data set are the acquisition under completely independent conditions, which has been experimentally validate for applicability across various environments for buried pipelines. Moreover, both the training and test dataset acquisition were performed using accelerometers on in-service buried pipelines, each with diameters of 1,100 mm, 1,200 mm, and 2,200 mm, extending over lengths ranging from approximately 200 to 500 meters. Despite the independent conditions of the datasets, our study yielded over 95% accuracy in early detection, with the results being in good agreement with the actual excavate locations.

Prediction of Residual Compressive Strength after Impact Based on Acoustic Emission Characteristic Parameters.

This study proposes a prediction method for residual compressive strength after impact based on the extreme gradient boosting model, focusing on composite laminates as the studied material system. Acoustic emission tests were conducted under controlled temperature and humidity conditions to collect characteristic parameters, establishing a mapping relationship between these parameters and residual compressive strength under small sample conditions. The model accurately predicted the residual compressive strength of the laminates after impact, with the coefficient of determination and root mean square error for the test set being 0.9910 and 2.9174, respectively. A comparison of the performance of the artificial neural network model and the extreme gradient boosting model shows that, in the case of small data volumes, the extreme gradient boosting model exhibits superior accuracy and robustness compared to the artificial neural network. Furthermore, the sensitivity of acoustic emission characteristic parameters is analyzed using the SHAP method, revealing that indicators such as peak amplitude, ring count, energy, and peak frequency significantly impact the prediction results of residual compressive strength. The machine-learning-based method for assessing the damage tolerance of composite laminates proposed in this paper utilizes the global monitoring advantages of acoustic emission technology to rapidly predict the residual compressive strength after the impact of composite laminates, providing a theoretical approach for online structural health monitoring of composite laminates. This method is applicable to various composite laminate structures under different impact conditions, demonstrating its broad applicability and reliability.