Damage Detection Research Articles

Dual-Modality Integrated Planar Sensor of ECT and EMT for CFRPs Damage Detection

Dual-Modality Integrated Planar Sensor of ECT and EMT for CFRPs Damage Detection

Real-Time Detection of Customer-Induced Damage in Printed Circuit Boards Using Mobile Devices and YOLO Detectors

The identification of Consumer-induced damage is essential for electronics manufacturers’ warranty programs. Consumer Induced Damage (CID) is any damage caused by an unauthorized person, including the consumer. The product warranty does not cover these damages, avoiding expenses in the manufacturer’s revenue. The consumer-induced damage warranty process is usually done manually by technically trained people. However, this task demands a lot of attention to detail, can be time-consuming, and is susceptible to human errors. With this in mind, this work presents an object detection model for low-computational-cost devices that uses computer vision and deep learning methods with YOLO detectors embedded in mobile devices to identify consumer-induced damages on printed circuit boards (PCB). We conducted sixteen experiments with four YOLO neural network architectures and successfully developed a mobile application for CID detection. Our best model achieved a mAP@0.5 of 33.1% and an average of 5.7 FPS on real mobile devices.

Synthesis and characterization of graphene-based electrocatalysts for the detection of damaged starch ratio

In this study, the detection of damaged starch ratio on synthesized graphene-based electrocatalysts were evaluated based on the iodine oxidation mechanism. Electrocatalysts were synthesized by three different electrochemical exfoliation techniques. Synthesized catalysts were tested in a three-electrode system with electrolytes mixed with flours at different damaged starch ratios (at 16.5, 25 and 30 UCD values). For the electrochemical tests related to iodine absorption, electrolyte solutions were prepared according to Medcalf and Gilles principle; cyclic voltammetry and chronoamperometry were performed at 50 mV/s in the range of 0–1 V vs. SCE; and at a potential of 1 V vs. SCE. The synthesized catalysts showed reversible reaction properties by giving anodic and cathodic peaks of iodine in the three-electrode system. The exfoliated graphene samples were characterized via standard tools such as Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Raman and UV-vis spectroscopy. After all, it was seen that the G2 type electrocatalyst has the highest sensitivity to iodine absorption and potential for use in damaged starch detection.

A weighted fuzzy inference method and application on wheel damage analysis

To mitigate the safety risks and economic losses caused by wheel damage, this paper proposes an interval valued fuzzy inference-based sound analysis method for wheel damage detection. Firstly, interval valued fuzzy sets are defined to represent various levels of damage severity. A similarity calculation method is then designed, based on the defined interval valued fuzzy sets, to assess the damage level of wheel components. Moreover, the OWA operator is employed to assign higher weights to key features while reducing the influence of noise or redundant features. Finally, a double-threshold interval valued fuzzy inference approach is proposed for comprehensive decision-making regarding the wheel damage degree. The proposed method is applied to wheel damage sound analysis, and a corresponding detection system is developed. Experimental results demonstrate that the proposed method outperforms existing techniques in detection accuracy, response speed, and robustness, and it is adaptable to various wheel operating environments.

Challenges in Damage Detection for Real-time Monitoring and Predictive Maintenance Methodology

Damage detection techniques offer a sophisticated and effective way to monitor and assess the health of engineering systems, but they come with challenges related to model accuracy, sensor data quality, and the detection of subtle or distributed damage. Advances in computational methods, sensor technology, and hybrid techniques continue to improve the practical application of these methods, making them increasingly relevant in real-time condition monitoring and predictive maintenance systems. The proposed methodology aims to develop damage detection systems that play a key role in enabling real-time monitoring and early identification of potential problems. This approach significantly reduces the need for conventional checks, leading to time and cost savings. By predicting the remaining service life of components, the methodology helps optimize maintenance schedules and prevent unexpected failures. Additionally, the use of smart materials and sensors enhances the accuracy and efficiency of damage detection, allowing more proactive, data-driven damage detection and monitoring systems.

Damage Detection and Identification on Elevator Systems Using Deep Learning Algorithms and Multibody Dynamics Models.

Timely damage detection on a mechanical system can prevent the appearance of catastrophic damage in it, as well as allow for better scheduling of its maintenance and repair process. For this purpose, multiple signal analysis methods have been developed to help identify anomalies in a system, through quantities such as vibrations or deformations in its critical components. In most applications, however, these data may be scarce or inexistent, hindering the overall process. For this purpose, a novel approach for damage detection and identification on elevator systems is developed in this work, where vibration data obtained through physical measurements and high-fidelity multibody dynamics models are combined with deep learning algorithms. High-quality training data are first generated through multibody dynamics simulations and are then combined with healthy state vibration measurements to train an ensemble of autoencoders and convolutional neural networks for damage detection and classification. A dedicated data acquisition system is then developed and integrated with an elevator cabin, allowing for condition monitoring through this novel methodology. The results indicate that the developed framework can accurately identify damages in the system, hinting at its potential as a powerful structural health monitoring tool for such applications, where manual damage localization would otherwise be considerably time-consuming.

Automated damage detection for open-air and underwater navigation infrastructure using generative AI-produced training data for deep learning models

Research has been continually growing toward the development of computer vision-based inspection tools for large-scale civil infrastructure; however, many deep learning techniques require large datasets to properly train models. Collecting field data can be costly and time-consuming, or may not be feasible, which has led to efforts to leverage synthetic data to supplement field data. Recent advances in text-to-image generative artificial intelligence (AI) offer the potential to quickly create realistic synthetic images of damaged infrastructure, including the complexities of the environment found in the field. In this study, the use of text-to-image generation to create a multiclass synthetic training dataset for inland navigation infrastructure is proposed, including damage of underwater structural components. Images of steel and concrete were generated that are representative of inland navigation infrastructure components. The images were labeled for semantic segmentation, and a model was trained using open-to-air and underwater scenes. The model trained using synthetic images was tested against field images, and the performance measured using recall, precision, and intersection over union was found to be comparable to a model trained using only field images. These results demonstrate that text-to-image generative AI tools were shown to be effective for generation of synthetic images with specifically defined conditions, saving time and cost, while providing a similar performance as the use of field-collected images. While intended for damage detection in large-scale civil infrastructure, this concept could be expanded to a number of areas as the generative AI models continue to improve.

Infrared thermography and 3D pavement surface unevenness measurement algorithm for damage assessment of concrete bridge decks

AbstractDeterioration of the concrete deck surface, including disintegration and delamination between the deck slab and pavement, presents significant challenges in bridge maintenance due to its hidden nature and the risk it poses to the deck's durability as damage progresses. Early detection is critical for preventing issues such as pothole formation and ensuring long‐term durability. However, traditional methods require core sampling, which often delays detection until damage is extensive. This study proposes a nondestructive approach combining infrared thermography (IRT) and laser‐based surface profiling to improve early detection of subsurface damage. IRT captures temperature variations on the pavement surface, detecting horizontal voids and moisture, while laser profiling refines the detection of deeper, progressive damage. By integrating these two methods, the technique offers a comprehensive assessment that single‐method approaches cannot provide. Field validation demonstrates that this method enables precise evaluation of bridge deck conditions, contributing to safer and more efficient bridge maintenance.

Building Damage Detection Using Deep Learning Architecture with Satellite Images: The Case of the 6 February 2023 Kahramanmaraş Earthquake

Turkey is located in a region with a high density of fault lines, which makes it susceptible to a significant earthquake risk. The Kahramanmaraş earthquake on February 6, 2023, was one of the most devastating in recent years, causing extensive damage and loss. This study aims to support post-disaster rapid response and rescue operations by using deep learning techniques to detect and classify damaged and intact buildings from satellite images. Satellite images of the Kahramanmaraş and Antakya regions, with a resolution of 8192x4537, were obtained via Google Earth Pro. The images were labeled as damaged or undamaged using the Labelme editor, which generated JSON format files for the labeled images. Using Google Colab, the JSON files and unlabeled images were merged, and buildings were cropped and categorized into two classes: damaged and undamaged. As a preprocessing step, interpolation was applied, resulting in 2211 images with a size of 128x128. A Convolutional Neural Network [2] algorithm was created using TensorFlow, a Python library, via Google Colab. The performance metrics, including accuracy, loss, F1 score, ROC curve, precision, recall, and confusion matrix values, were compared based on the experiments.

Signal-Centric Framework Based on Probability of Detection for Real-Time Reliability of Concrete Damage Inspection

Passive nondestructive testing (NDT) methods allow one to detect damage by the energies emitted from the internal processes. While the test conditions can be controlled and repeatable, obtained data are random, and the probability of detection (PoD) is affected. However, in concrete with complex fracture behavior, factors such as signal attenuation, sensor-damage distance, and test configuration influence the reliability of the test. The conventional practice of proceeding without assessing credibility prevents the ability to determine whether a configuration modification is required, necessitating reassessment. The main objective of this study is to develop a signal-centric framework to enhance the real-time reliability of inspection by investigating the PoD of acoustic emission (AE), a widely used passive NDT method for the real-time monitoring of structures. This study’s purpose is to evaluate the mechanical processes and the passive signal responses, emphasizing the detectability of cracking in concrete with two PoD approaches, namely, amplitude- and energy-based PoDs. Additionally, critical signal signatures, namely, signal-to-noise ratio (SNR) and frequency, were pinpointed for their direct influence on the detectability of the crack. With the outcomes obtained, a novel framework, which aims to provide an adaptive evaluation of the PoD of the technique, was suggested to achieve the desired quality in the damage detection of structures.

Application of wavelet transform for damage detection in steel nodes of statically loaded truss girders

Application of wavelet transform for damage detection in steel nodes of statically loaded truss girders

Retinal Microperimetry as a Novel Tool for Early Detection of Subclinical Cognitive Dysfunction and Brain Damage in Type 1 Diabetes: A Pilot Study.

Retinal microperimetry (MPR) is a non-invasive method that measures retinal light sensitivity (RS) and gaze fixation stability (GFS). MPR has been described as a marker of cognitive impairment in people with Type 2 diabetes, but it has never been assessed in people with Type 1 diabetes (T1D). Our group described subclinical cognitive alterations, structural brain differences, and increased levels of light chain neurofilament (NfL) in people with T1D and impaired awareness of hypoglycaemia. To measure RS and GFS using MPR in individuals with T1D and evaluate its correlation with neuropsychological assessment, plasma NfL levels and CGM-derived glucometric parameters. to evaluate the possible differences of RS and GFS in people with T1D depending on hypoglycaemia awareness. Pilot observational study, people with T1D without clinical cognitive impairment, moderate-severe retinopathy or glaucoma. MPR was performed with MAIA3. A total of 30 subjects were studied: 40% women, age 58 ± 11 years; T1D duration 31 ± 9 years, mild retinopathy 33%. RS was 27.5 dB (26.1-28.3) and GFS(%) 97.6% (93.5%-99.5%). We found a correlation between RS and memory alteration tests (p = 0.016) and between GFS(%) and a composite of attention and executive neuropsychological tests (p = 0.025). An inverse correlation between GFS and time below range was found. No correlation was found with NfL. This first exploratory study in people with T1D supports the potential utility of MPR as a screening tool for subclinical neurocognitive alterations in this population.

Medical test and employee's autonomy. Confidentiality of data and non-discrimination.

Monitoring health is one of the basic principles of Occupational Health and Safety. The main objective of this monitoring will be the detection of possible damage to health arising from work. They try to discover the effects that the inherent risks with the work may cause the worker, which will show, given the case, through an alteration of health or the state of organic and functional state, both physically and mentally. Regarding the monitoring of health, there are many and varied issues to be raised concerning the right to personal privacy. Thus, notably, whether there was effective consensus, and whether, in particular, the object and the purpose of the medical examination meant a disregard of the statutory scheme, an excess or even a failure to comply with the terms of the Spanish Constitution. The limits imposed by the law makers to the productive unit will play a key role in identifying the content of the company obligation to monitor health, being in many cases guarantees of constitutional rights. Therefore, the methods of health monitoring and control of workers will always be carried out respecting the privacy and dignity of the worker as well as the confidentiality of all information related to their health.

Damage location and area measurement of aviation functional surface via neural radiance field and improved Yolov8 network

To realize high-precision intelligent detection, location and area measurement of aviation functional surface damage, a damage location and area measurement method combining neural radiance field and improved Yolov8 network is proposed in this paper. The high-fidelity NeRF (Neural Radiance Field) and 3DGS (3D Gaussian Splatting) models are trained by acquired multi-view optical images of damaged functional surfaces. The rendered new-view images are used as a new data augmentation method to enhance the training effect of Yolov8 network. The network architecture of Yolov8 model is improved. The backbone is replaced with the latest StarNet feature extraction network, and the context feature fusion module (CFFM) proposed in this paper is used for feature fusion enhancement. The improved context-guide multi-head self-attention (CG-MHSA) is added to the detection Head. The comparison and ablation experiment results show that the improved module proposed in this paper has a good effect on the improvement of Yolov8 model, and improves the damage detection ability and location ability of the model. The application experiment results verify the effectiveness of the proposed method for calculating the damage area of a plane/camber surface, and the accuracy of the damage area measurement is high.

The Detection of an Inter-Turn Short-Circuit Fault in a Brushless Permanent Magnet Motor with Different Winding Configurations

This article is about the detection of a partial inter-turn short-circuit fault in a brushless motor with permanent magnets (BLPMM). The detection of a single inter-turn short circuit is a difficult issue. The authors of this article tested the sensitivity of the tested powertrain damage detection method. The diagnostic method developed for BLPMM allows for any configuration of the motor winding. A number of analysed configurations have been applied for the combined star–delta connection (YD). For the combined star–delta connection Y/D, the problem of partial short circuit at two locations was analysed. In the first case, this was the short-circuit winding phenomena in the star part (SC1). In the second case, the short circuit was connected in part to the delta (SC2). A mathematical model has been developed that takes into account both the type of connection and the chance of a partial short circuit of the coil. Based on numerical calculations, the sensitivity of diagnostic methods is designated for both cases. Furthermore, the impact of partial short circuits on motor performance was also examined. The effect of a partial inter-turn short-circuit fault on motor parameters was also determined. Laboratory verification was carried out.

Damage Localization and Severity Assessment in Composite Structures Using Deep Learning Based on Lamb Waves.

In composite structures, the precise identification and localization of damage is necessary to preserve structural integrity in applications across such fields as aeronautical, civil, and mechanical engineering. This study presents a deep learning (DL)-assisted framework for simultaneous damage localization and severity assessment in composite structures using Lamb waves (LWs). Previous studies have often focused on either damage detection or localization in composite structures. In contrast, this study aims to perform damage detection, severity assessment, and localization using independent DL models. Three DL models, namely the artificial neural network (ANN), convolutional neural network (CNN), and gated recurrent unit (GRU), are compared. To assess their damage detection and localization capabilities. Moreover, zero-mean Gaussian noise is introduced as a data augmentation technique to address the variability and noise inherent in LW signals, improving the generalization capability of the DL models. The proposed framework is validated on a composite plate with four piezoelectric transducers, one at each corner, and achieves high accuracy in both damage localization and severity assessment, offering an effective solution for real-time structural health monitoring. This dual-function approach provides a scalable data-driven method to evaluate composite structures, with applications in predictive maintenance and reliability assurance in critical engineering systems.

EFFICIENCY OF USE CHARACTERISTICS OF DAMPING OF TURBINE BLADES FOR CRACK DIAGNOSTICS

During operation, turbine blades are subjected to intense mechanical (static and dynamic) and thermal loads in a corrosive and aggressive environment. The consequence of such loading is the gradual accumulation of diffuse fatigue damage, which eventually becomes localized as a fatigue crack. When the crack reaches a critical size, there is a danger of blade destruction with catastrophic consequences for the entire turbine. Timely detection of blade damage is possible at the stage of turbine repair using vibration diagnostics. The purpose of the work is to create an analytical approach that allows modeling the change in the vibration damping characteristics of a turbine blade with a fatigue crack, as well as the study of the influence of the crack parameters and the geometric parameters of the blade on the vibration damping characteristics of the blade to assess the effectiveness of vibration diagnostics of damage. The results of the study of the effect of a surface transverse crack on the damping characteristics of turbine blade oscillations are presented. As a result of the experimental and analytical study, the effect of the crack parameters (size and location of the crack) and the geometric parameters of the blade on the damping characteristics of blade vibrations was established. Vibration diagnostic of turbine blades, based on a change in the damping characteristics of its vibrations, is a sufficiently effective diagnostic feature for detecting relatively small cracks, the size of which does not pose a threat to the integrity of the blades. Given that the materials used for the manufacture of modern steam turbines and gas turbine engines have a rather low damping capacity, the use of changes in the damping characteristics of blade oscillations as a vibration diagnostic sign of damage is promising. Further work involves the study of the effect of cracks that appear on the edges of the blades on the damping characteristics of blade vibrations

Earthquake-induced building damage detection using the fusion of optical and radar data in intelligent systems

Earthquake-induced building damage detection using the fusion of optical and radar data in intelligent systems

Structural Health Monitoring of Nuclear Site Facilities Using Optimal Sensor Placement for Damage Detection and Prediction of Failure

Structural Health Monitoring of Nuclear Site Facilities Using Optimal Sensor Placement for Damage Detection and Prediction of Failure

Mechanochemical Release of 9,10-Diphenylanthracene via Flex-Activation of Its 1,4-Diels-Alder Adduct.

Flex-activated mechanophores capable of releasing small molecules utilize bond bending to facilitate their mechanochemical activation without compromising the overall macromolecular architecture, which have great potential in various applications. However, the development of such mechanophores remains underexplored. Here we report a novel flex-activated mechanophore based on the 1,4-Diels-Alder (DA) adduct of 9,10-diphenylanthracene (DPA) with acetylenedicarboxylate (ADC). Compression of the mechanophore-crosslinked polymer networks mechanochemically activates the weakly fluorescent DPA-ADC mechanophores to undergo a retro-DA reaction in accompany with the release of highly fluorescent DPA molecules (quantum yield close to unity), as confirmed by fluorescence spectroscopy and gas chromatography-mass spectrometry (GC-MS) analysis. As a new member of the small family of flex-activated mechanophores, this fluorogenic DPA-ADC mechanophore possesses promising applications in stress sensing and damage detection.