Damage Detection Framework Research Articles

Noise robust damage detection of laminated composites using multichannel wavelet-enhanced deep learning model

This paper presents a noise-robust damage detection framework for composite structures via a commonly used vibration-based non-destructive testing (NDT) method. Recently, deep learning-based models have shown promising performance in the autonomous damage detection of laminated composites; however, the poor noise robustness of these models has plagued data-driven damage detection. Moreover, none of the existing studies on damage detection in laminated composites focus on noise-robust deep learning models with high generalization ability. Therefore, this study proposes a hybrid deep learning framework called a multi-channel convolutional autoencoder-support vector machine (MC-CAE-SVM) based on empirical mode decomposition (EMD) and correlation analysis for noise-robust damage detection. This framework aims to first decompose the vibrational signal from multiple health states into intrinsic mode functions (IMFs). Secondly, highly correlated IMFs were extracted using correlation analysis to remove noisy IMFs. Finally, these IMFs were transformed into a time-frequency representation using continuous wavelet transform (CWT) and input to the MC-CAE-SVM model for feature learning and damage detection. Additionally, the accuracy and sensitivity of the model to damage are enhanced by optimizing the MC-CAE-SVM model hyperparameters. Moreover, anti-noise analysis is performed to check the noise-robustness of the proposed model by incorporating noise at various levels. The results showed that the proposed model can provide better damage detection performance compared to conventional models with excellent noise robustness.

Passive control and damage assessment with a pendulum tuned mass damper using mean square equations and second-order eigen-perturbation techniques

This paper proposes a novel framework for damage detection and retuning of a Pendulum Tuned Mass Damper (PTMD) addressing effects of stochasticity via mean-square equations and establishing rapid change detection. The framework was made possible via a Stochastic Differential Equation (SDE) formulation of the equations of motion considering a two degree of freedom base and structure system equipped with a PTMD. The system was assumed to be excited at the base with Gaussian white noise and the solutions were computed using a stochastic integration scheme. A Second Order Eigen-Perturbation (SOPS) estimate is used to carry out detection of sudden stiffness reduction in the base floor of the structure. Once damage is detected, mean square equations on monitored displacements were used to retune the PTMD by adapting the length of the pendulum. It was found that damage could be detected rapidly and successive retunings were able to minimize the mean square displacement of the damaged structure and stabilise it over time.

A model-based deep learning framework for damage classification and detection in polycarbonate infused with AEROSIL under dynamic loading conditions

Composite 3D printing is a significant engineering application owing to its robustness, ability to achieve complex geometries, and ease of use. Polycarbonate, particularly when infused with AEROSIL, is an interesting thermoplastic and potential candidate for 3D printing with enhanced properties. The primary objective of this research is to develop a new model-based deep-learning framework to classify and detect damage in this material under dynamic loading conditions. To achieve this, a FASTCAM high-speed camera was placed in front of the SHPB test setup to capture dynamic damage. The test results were then used as label inputs for training the advanced deep learning algorithms, focusing on dense image recognition techniques for detailed damage analysis. The study involved a series of fully convolutional networks (FCNs), evaluating semantic segmentation with U-Net and instance segmentation with state-of-the-art frameworks such as YOLOv8 and Mask R–CNN. A comparative analysis revealed that deep learning models outperform traditional methods, providing efficient and accurate damage classification and detection. The U-Net model demonstrated the ability to recognize cubes and bars but was limited in detecting minor damage regardless of size. YOLO-V8, which specializes in case segmentation, achieved remarkable performance in detecting significant damage but struggled to accurately identify minor damage. By leveraging deep learning techniques, this study enables an efficient and accurate damage assessment, which is crucial for ensuring the reliability and safety of composite structures in various industries.

Structural damage detection and localization via an unsupervised anomaly detection method

This study introduces an unsupervised machine learning framework for damage detection and localization in Structural Health Monitoring (SHM), leveraging dynamic graph convolutional neural networks and Transformer networks. This approach is specifically tailored to overcome the challenge of limited labeled data in SHM, enabling precise analysis and feature synthesis from sensor-derived time series data for accurate damage identification. Incorporating a novel ‘localization score’ enhances the framework’s precision in pinpointing structural damages by integrating data-driven insights with a physics-informed understanding of structural dynamics. Extensive validations on diverse structures, including a benchmark steel structure and a real-world cable-stayed bridge, underscore the framework’s effectiveness in anomaly detection and damage localization, showcasing its potential to safeguard critical infrastructure through advanced data-effective machine learning techniques.

Multi-modal deep learning framework for damage detection in social media posts.

In crisis management, quickly identifying and helping affected individuals is key, especially when there is limited information about the survivors' conditions. Traditional emergency systems often face issues with reachability and handling large volumes of requests. Social media has become crucial in disaster response, providing important information and aiding in rescues when standard communication systems fail. Due to the large amount of data generated on social media during emergencies, there is a need for automated systems to process this information effectively and help improve emergency responses, potentially saving lives. Therefore, accurately understanding visual scenes and their meanings is important for identifying damage and obtaining useful information. Our research introduces a framework for detecting damage in social media posts, combining the Bidirectional Encoder Representations from Transformers (BERT) architecture with advanced convolutional processing. This framework includes a BERT-based network for analyzing text and multiple convolutional neural network blocks for processing images. The results show that this combination is very effective, outperforming existing methods in accuracy, recall, and F1 score. In the future, this method could be enhanced by including more types of information, such as human voices or background sounds, to improve its prediction efficiency.

Apply frequency response function schemes for damage detection in composite nanoscale-pipes under transient conditions

Knowledge of Nanoscale-structures sustainability is strongly associated with the reliability and the performances of Nano-electro-mechanical systems such as Nanoactuators and Nano-sensors that are widely used in bio-applications. Therefore, the damages in these types of infinitesimal systems must be quickly and accurately detected, located, and repaired. In this study, a novel damage detection framework in Carbon Fiber Reinforced Polymer (CFRP) composite Nanoscale-Pipes (CNP) subjected to the fatigue effect of water internal pressure and thermal effect via Nanoscale-electrical capacitance sensors (NECS). First, a finite element model (FEM) of CNP damage is established, then the electrical potential difference (EPD) between the Nanoscale-electrodes (NEs) pairs before and after damage is measured. The distributed NECS measuring nano-electrodes are installed around the CNP and load the transient external excitations between each other. It is proposed and applied the transfer function (TF) of the CNP as an "open loop control" system to reflect the evolution of damage. The accuracy and reliability of the suggested technology are verified through the experimental results available in the literature. The results show that when the damage under NEs pairs has begun to grow, the signal amplitude value will change suddenly and sensitively, which demonstrates the effectiveness of the proposed method and the good potential for engineering applications.

Footbridge damage detection using smartphone-recorded responses of micromobility and convolutional neural networks

This paper presents a footbridge damage detection and classification framework using smartphone-recorded responses of micromobility and deep learning techniques. Time–frequency representations (TFRs) of scooter vibrations are employed to detect and classify footbridge damage severities using a Two-Dimensional Convolutional Neural Network (2D CNN). A One-Dimensional (1D) CNN using scooter frequency spectra was also investigated for comparison. The effectiveness of the method was verified using a numerical model of scooter-footbridge interactions and field tests on a real footbridge. The results indicated that both CNNs were sensitive to footbridge frequency alterations caused by damage in the numerical simulations. Nevertheless, the performance of the 1D CNN experienced a substantial decline in field tests involving stochastic influencing factors, whereas the accuracy of damage classification using the 2D CNN remained high. Finally, reasonable interpretations for the superior performance of the 2D CNN are provided using Shapley Additive Explanations (SHAP) values.

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.

Cascaded framework for earthquake building damage detection combining spatial and frequency domain feature integration

Abstract. Building collapse is a major cause of casualties after an earthquake, so accurately extracting building damage information is critical for post-earthquake assessment and rescue. Currently, most deep learning methods focus on the end-to-end detection of building collapse. However, in real-world earthquake scenarios, the end-to-end computational process often lacks flexibility and struggles to meet the requirements of rapid emergency response. To address this issue, this paper proposes a cascaded framework that combines pre-earthquake building extraction and post-earthquake building damage classification. The proposed framework includes two sections: (1) Progressive building semantic segmentation model in the joint frequency domain. This model is designed to accurately extract buildings prior to an earthquake, with the goal of minimizing error propagation throughout the cascading process. The model addresses the spatial similarity of buildings under complicated backgrounds, as well as the high internal heterogeneity of buildings, by utilizing frequency domain techniques. It compensates for the shortcomings of traditional models in terms of incomplete information extraction through the effective integration of global and local information. Finally, the model employs edge priors for edge regularization. (2) Rapid building damage classification process. Based on the accurate building extraction results, a fast and efficient classification process is developed. This process uses a simple and lightweight classification network to effectively extract building damage information caused by the earthquake. The superiority of the proposed framework is validated through comparison with traditional cascading architectures and end-to-end models. The results show that the cascading framework not only provides accurate pre-earthquake building extraction, but also enables efficient and accurate post-earthquake damage classification, which meets the requirements of rapid post-earthquake emergency response. This balance of accuracy and speed is essential for effective disaster management and recovery.

Multi-visual modality micro drone-based structural damage detection

Accurate detection and resilience of object detectors in structural damage detection are important in ensuring the continuous use of civil infrastructure. However, achieving robustness in object detectors remains a persistent challenge, impacting their ability to generalize effectively. This study proposes DetectorX, a robust framework for structural damage detection coupled with a micro drone. DetectorX addresses the challenges of object detector robustness by incorporating two innovative modules: a stem block and a spiral pooling technique. The stem block introduces a dynamic visual modality by leveraging the outputs of two Deep Convolutional Neural Network (DCNN) models. The framework employs the proposed event-based reward reinforcement learning to constrain the actions of a parent and child DCNN model leading to a reward. This results in the induction of two dynamic visual modalities alongside the Red, Green, and Blue (RGB) data. This enhancement significantly augments DetectorX's perception and adaptability in diverse environmental situations. Further, a spiral pooling technique, an online image augmentation method, strengthens the framework by increasing feature representations by concatenating spiraled and average/max pooled features. In three extensive experiments: (1) comparative and (2) robustness, which use the Pacific Earthquake Engineering Research Hub ImageNet dataset, and (3) field-experiment, DetectorX performed satisfactorily across varying metrics, including precision (0.88), recall (0.84), average precision (0.91), mean average precision (0.76), and mean average recall (0.73), compared to the competing detectors including You Only Look Once X-medium (YOLOX-m) and others. The study's findings indicate that DetectorX can provide satisfactory results and demonstrate resilience in challenging environments.

Damage Detection in Bridge Structures through Compressed Sensing of Crowdsourced Smartphone Data

Traditional bridge health monitoring methods that necessitate sensor installation are not only costly but also time-consuming. In contrast, utilizing smartphone data collected from vehicles as they traverse bridges offers an efficient and cost-effective alternative. This paper introduces a cutting-edge damage detection framework for indirect monitoring of bridge structures, leveraging a substantial volume of acceleration data collected from smartphones in vehicles passing over the bridge. Our innovative approach addresses the challenge of collecting and transmitting high-frequency data while preserving smartphone battery life and data plans through the integration of compressed sensing (CS) into the crowdsensing-based monitoring framework. CS employs random sampling and signal recovery from a significantly reduced number of samples compared to the requirements of the Nyquist–Shannon sampling theorem. In the proposed framework, acceleration signals from vehicles are initially acquired using smartphone sensors, undergo compression, and are then transmitted for signal reconstruction. Subsequently, feature extraction and dimensionality reduction are performed using Mel-frequency cepstral coefficients and principal component analysis. Damage indexes are computed based on the dissimilarity between probability distribution functions utilizing the Wasserstein distance metric. The efficacy of the proposed methodology in bridge monitoring has been substantiated through the utilization of numerical models and a lab-scale bridge. Furthermore, the feasibility of implementing the framework in a real-world application has been investigated, leveraging the smartphone data from 102 vehicle trips on the Golden Gate Bridge. The results demonstrate that damage detection using the reconstructed signals obtained through compressed sensing achieves comparable performance to that obtained with the original data sampled at the Nyquist measurement sampling rate. However, it is observed that to retain severity information within the signals for accurate damage severity identification, the compression level should be limited to 20%. These findings affirm that compressed sensing significantly reduces the data collection requirements for crowdsensing-based monitoring applications, without compromising the accuracy of damage detection while preserving essential damage-sensitive information within the dataset.

Multi-task Framework for Vibration-Based Structural Damage Detection of Spatial Truss Structure Using Graph Learning

Multi-task Framework for Vibration-Based Structural Damage Detection of Spatial Truss Structure Using Graph Learning

Optimal Sensor Placement for Vibration-Based Damage Localization Using the Transmittance Function.

A methodology for optimal sensor placement is presented in the current work. This methodology incorporates a damage detection framework with simulated damage scenarios and can efficiently provide the optimal combination of sensor locations for vibration-based damage localization purposes. A classic approach in vibration-based methods is to decide the sensor locations based, either directly or indirectly, on the modal information of the structure. While these methodologies perform very well, they are designed to predict the optimal locations of single sensors. The presented methodology relies on the Transmittance Function. This metric requires only output information from the testing procedure and is calculated between two acceleration signals from the structure. As such, the outcome of the presented method is a list of optimal combinations of sensor locations. This is achieved by incorporating a damage detection framework that has been developed and tested in the past. On top of this framework, a new layer is added that evaluates the sensitivity and effectiveness of all possible sensor location combinations with simulated damage scenarios. The effectiveness of each sensor combination is evaluated by calling the damage detection framework and feeding as inputs only a specific combination of acceleration signals each time. The final output is a list of sensor combinations sorted by their sensitivity.

Unsupervised deep learning framework for temperature-compensated damage assessment using ultrasonic guided waves on edge device

Fueled by the rapid development of machine learning (ML) and greater access to cloud computing and graphics processing units, various deep learning based models have been proposed for improving performance of ultrasonic guided wave structural health monitoring (GW-SHM) systems, especially to counter complexity and heterogeneity in data due to varying environmental factors (e.g., temperature) and types of damages. Such models typically comprise of millions of trainable parameters, and therefore add to cost of deployment due to requirements of cloud connectivity and processing, thus limiting the scale of deployment of GW-SHM. In this work, we propose an alternative solution that leverages TinyML framework for development of light-weight ML models that could be directly deployed on embedded edge devices. The utility of our solution is illustrated by presenting an unsupervised learning framework for damage detection in honeycomb composite sandwich structure with disbond and delamination type of damages, validated using data generated by finite element simulations and experiments performed at various temperatures in the range 0–90 °C. We demonstrate a fully-integrated solution using a Xilinx Artix-7 FPGA for data acquisition and control, and edge-inference of damage. Despite the limited number of features, the lightweight model shows reasonably high accuracy, thereby enabling detection of small size defects with improved sensitivity on an edge device for online GW-SHM.

A framework for damage detection in AIS data based on clustering and multi-label classification

Automatic Identification System (AIS) is a telecommunication system that allows marine equipment (ships, shore-based stations) to communicate and transmit information regarding their trajectory (position, speed, course, etc.), so that other ships are aware of their presence and collision between vessels can be avoided. Unfortunately, AIS struggles against some technical limitations (relatively short range of its terrestrial segment that led to the introduction of satellite AIS segment, that deals with new problems, namely packet collisions) that leads to parts of the AIS data being damaged during transmission. Therefore, to maintain the proper level of maritime safety and security, there is a need for reconstruction of such damaged AIS data, so that ships keep receiving appropriate updates from their neighbours. In this paper we propose a machine-learning based approach for AIS data reconstruction, with one of its stages – detection of damaged data that requires reconstruction – being its main focus. The characteristics of the proposed approach is presented, together with the results of several computational experiments that measure its performance. This article is an extended version of a paper that was presented at the International Conference on Computational Science in 2023 (ICCS-2023) (Szarmach and Czarnowski, 2023), enriched with the results of additional computational experiments.

Real-time canola damage detection: An end-to-end framework with semi-automatic crusher and lightweight ShuffleNetV2_YOLOv5s

The current method employed in detecting damages present in canola kernels is inefficient and time-consuming. The sample preparation process is laborious and the judgment between sound and damaged seeds using visual methods is prone to errors as they are small and indistinguishable. To date, there is no hardware and supporting software solution that can expedite this time-consuming yet crucial task. This study demonstrates an end-to-end damage detection framework that has two components: a semi-automatic machine to crush the canola seeds to prepare the samples rapidly for detecting the damages and a supporting object detection algorithm based on a compressed YOLOv5s model. The lightweight detection model was deployed in an Edge-AI device and integrated with the crusher to detect the damages in real-time. Two Convolutional Neural Network (CNN) architectures, viz. ShuffleNetV2 and MobileNetV3 were compared in terms of model parameters, computational cost, and model size to replace the standard CSPDarknet53 CNN backbone present in YOLOv5s. The best-performing model was deployed into an NVIDIA Jetson Nano embedded device for real-time inferencing. Results indicate that in terms of model size, the ShuffleNetV2_YOLOv5s model was 56.5 and 80.4 % smaller than the MobileNetV3_YOLOv5s and the baseline YOLOv5s models, respectively. While, in terms of computational cost, the ShuffleNetV2_YOLOv5s model was 64.1 % and 99.5 % less expensive than the MobileNetV3_YOLOv5s and the baseline YOLOv5s models, respectively. This study demonstrated an end-to-end framework for detecting damages in canola supported by a real-time and lightweight damage detection model.

A digital twin-based framework for damage detection of a floating wind turbine structure under various loading conditions based on deep learning approach

Engineering has many necessary fields, and Structural Health Monitoring (SHM) is one of the most important of them. Sometimes in industrial environments, it is difficult and even impossible to collect data containing different real damages. Therefore, the problem of data acquisition represents a primary challenge in designing damage detection systems. The application of digital twin methods based on simulated models and/or Machine Learning (ML) models is a practical way to solve this problem. In this approach, a digital twin is generated for a compromised structure, utilizing a physics-based model to analyze diverse damage scenarios. Subsequently, an ML model is trained using data extracted from the physics-based model, functioning as the digital twin. This research proposes a method based on a digital twin for detecting damages in structures. The data produced from a Floating Wind Turbine (FWT) model was used to evaluate the performance of the proposed digital twin-based method. For this purpose, the FWT structure was simulated using a numerical model to address the data collection problem in the face of various uncertainties, such as changing loading conditions. In line with the concept of digital twin and to reduce the computational time, a Deep Convolution Long Short-Term Memory Neural Network (DCLSTMNN) model was designed and trained only with the frequency data of various scenarios of the simulated FWT model under constant loads (deterministic loads, including constant wind speed and airy wave model) to learn the damage-sensitive features. Then, to demonstrate the robustness of the proposed model under different uncertainties, the DCLSTMNN model was evaluated using the frequency data of the simulated FWT model under variable loading conditions (including Kaimal wind model and JONSWAP wave theory). Some vibration response components unrelated to the nature of the FWT model were removed using the Complete Ensemble Empirical Mode Decomposition (CEEMD) method. Then, the reconstructed vibration responses were used to create the frequency data using the Frequency Domain Decomposition (FDD) technique. The study results show that the proposed digital twin-based method can detect the location and severity of damage more accurately than other comparable methods despite various uncertainties.

Active sensing ultrasonic guided wave-based damage diagnosis via stochastic stationary time-series models

In the context of acousto-ultrasonic guided wave-based structural health monitoring, a statistical damage detection and identification (collectively referred to as damage diagnosis) framework for metallic and composite materials is proposed. Stochastic stationary time-series autoregressive (AR) models are used to model the ultrasonic wave propagation between piezoelectric actuator-sensor pairs on structural components and enable the damage diagnosis process via the use of the AR estimated parameters and corresponding covariance matrices. The proposed method exploits guided wave signals including the reflection parts, and thus the extraction of the S0 and/or A0 modes is not necessary, while the statistical properties and variation of estimated model parameters with respect to damage intersecting and non-intersecting wave propagation paths are presented and assessed. To investigate the method’s performance and robustness, two variations are proposed based on the singular value decomposition and principal component analysis. The obtained modified AR parameter vectors are then used to estimate appropriate statistical characteristic quantities used to enable the damage detection and identification tasks. The diagnosis is based on properly defined statistical hypotheses decision-making schemes and predetermined type I error probabilities. The performance and applicability of the method are explored experimentally via a series of tests on aluminum and composite coupons under various damage scenarios for damage intersecting and non-intersecting paths. The results of the present study demonstrated the effectiveness and robustness of the proposed modeling and diagnostic framework for guided wave-based damage diagnosis that can be implemented in a potentially automated way.

A percussion method with attention mechanism and feature aggregation for detecting internal cavities in timber

Timber structures have been widely utilized in the field of construction. Nevertheless, these structures are susceptible to environmental factors that can cause internal damage, including cavities, cracks, and corrosion, which can significantly compromise their structural integrity and safety. However, the detection of such internal damages is often challenging due to their concealed nature. Existing methods for detecting internal damage in timber structures suffer from low detection efficiency and recognition accuracy. This study proposes an end-to-end damage detection framework that integrates emphasized channel attention, propagation, and aggregation-time-delay neural network (ECAPA-TDNN) with percussion techniques. This framework is designed to evaluate the internal damage status of timber structures. The paper also introduces a method for creating internal voids within timber structures and fabricates timber specimens containing various sizes of internal damage. Vibration responses at different locations on the specimens are obtained using percussion techniques and serve as input data for the network. The results indicated that the ECAPA-TDNN architecture could effectively identifies damage within the timber structure and accurately distinguish between different sizes of damage, furnishing superlative recognition performance transcending prevailing benchmark models. This study elucidates the potential of percussion-based techniques and deep learning methods in the damage detection field, as well as the possibility for the proposed method to be extended to other applications, including transformer inspection, vehicle vibration monitoring, and medical treatment.

An Improved Impact Damage Monitoring Method for High-Speed Trains Using Lamb Waves and Multi-Task Learning

High-speed trains may be subjected to various forms of physical impacts during long-term operation, causing structural damage and endangering driving safety. Therefore, impact damage monitoring remains a daunting challenge for the stable operation of high-speed train structures. The existing methods cannot simultaneously detect the location and severity of impact damage, which poses challenges to structural integrity assessment and preventive maintenance. This article proposes an impact damage monitoring method based on multi-task 2D-CNN. Sensor data fusion is performed using a 2D image processing method to convert a 1D impact damage signal into a 2D grayscale image. The fused grayscale image contains information related to the location and severity of impact damage. A damage detection framework was established using multi-task 2D-CNN for feature extraction, impact location classification, and impact energy quantification. This model can learn the commonalities and characteristics of each task by sharing network structure and parameters and can effectively improve the accuracy of each task. Compared with single-task learning, multi-task learning performs better on the metrics of the impact location task recognizing the impact energy task and reduces the training time by 30.83%. With a reduced number of samples, the performance of multi-task learning is more stable and can still effectively identify the location and severity of impact damage.