Damage Detection Research Articles

Vibration-based structural damage detection using FRF and modal constant curvature

Vibration-based structural damage detection using FRF and modal constant curvature

Comparing Gaussian process enhanced grey-box approaches to detect damage in unknown environmental conditions due to climate change

In vibration-based structural health monitoring (SHM), it is well known that environmental and operational variations (EOVs) affect the dynamic response of the structure of interest. This fact makes it difficult to distinguish between structural changes caused by damage and those caused by EOVs. In SHM, this issue is addressed by data normalisation, whereby machine learning techniques are commonly applied. However, ensuring their accuracy necessitates capturing a comprehensive range of EOVs within the training data. Collecting these is inherently challenging for real-world applications, especially with new EOV states emerging due to climate change. This study’s unique contribution is applying and comparing two grey-box models based on Gaussian process (GP) regression to remove EOVs with low data coverage and demonstrate their efficiency for damage detection with an open-access benchmark dataset. To this end, the first two bending mode natural frequencies – used as damage-sensitive features – of the Leibniz University Test Structure for Monitoring (LUMO), an outdoor lattice tower, are mapped. Two approaches to embedding physical knowledge in the GP are investigated. The first approach incorporates knowledge through the mean function, while the second involves the selection or design of a kernel. Subsequently, the two approaches are compared with a black-box and a white-box model. For long-term SHM, the repair of a damage mechanism is accounted for by the normal-condition alignment scheme, and damage detection is performed using the Mahalanobis distance. The study demonstrates that applying grey-box models contributes to a more reliable representation of the variations caused by unknown EOVs than pure black-box models, thereby improving damage detectability with sparse and incomplete training data due to climate change. However, as the dependencies modelled for LUMO are primarily linear, further research is required to assess the applicability of these findings to structures where dependencies are expected to be non-linear.

Machinery structural crack damage detection based on acoustic signal with ICEEMDAN and sensitive IMF fuzzy entropy

Abstract With the increasingly urgent demand on the reliability of mechanical equipment, in the process of production and service, it is of vital importance to apply precise and efficient crack damage detection on the critical structures. To overcome the shortcomings of existing damage detection methods and meet the urgent needs of engineering practice, by analyzing acoustic signal, a novel machinery structural crack damage detection method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and sensitive intrinsic mode function (IMF) fuzzy entropy is proposed in this paper. Firstly, redundant second-generation wavelet denoising strategy based on neighborhood correlation is applied on the raw acoustic signal. Then, the pre-denoised acoustic signal is decomposed by ICEEMDAN to obtain a set of IMFs, and the fuzzy entropies of the first eight IMFs are calculated to reflect the structural crack damage states. Finally, with distance evaluation technique, the most sensitive IMF fuzzy entropy is selected and defined as the damage index to assess the structural crack damage levels. Effectiveness of the proposed method is validated by two case studies as for the crack damage detection of different machinery structures. The results show that the defined damage index is not only sensitive to the occurrence of structural crack damage, but also decreases obviously with the increasing damage level and is not affected by damage location.

Ultrasonic guided wave detection of point rail damage of high-speed railway turnout

The precise and efficient identification of damage in point rails of turnouts represents a critical and challenging technological issue that requires immediate attention. Guided wave inspection, as a non-destructive testing method, holds significant potential for practical applications in the research field focused on the identification of damage in point rails of high-speed railway turnouts. This study integrates rigorous theoretical analysis, advanced numerical simulation techniques, and meticulous experimental investigations to comprehensively investigate the excitation, propagation, and reception processes of ultrasonic-guided waves within the point rail. By strategically applying excitation signals at optimized positions specifically tailored for point rail detection, employing a frequency of 30 kHz, appropriate excitation modes are carefully selected. Defect identification within the extended point rail is performed from a dual perspective, encompassing both cross-correlation analysis and time-frequency analysis. The obtained results substantiate that when employing the cross-correlation coefficient to detect cracks in the elongated point rail, the sensitivity of crack detection is significantly higher in the rail bottom region as opposed to the rail head region. Furthermore, through the analysis of variations in the cross-correlation coefficient and the application of wavelet transformations to the guided wave signals, it is not only feasible to ascertain the presence and location of damages within the rail base region of the extended point rail but also to evaluate the relative size of the cracks. The findings of this research contribute a robust theoretical foundation for the subsequent analysis and evaluation of damage detection in high-speed turnout point rails.

Guided Waves in Composite Overwrapped Pressure Vessels and Considerations for Sensor Placement Toward Structural Health Monitoring—An Experimental Study

Abstract The utilization of composite overwrapped pressure vessels (COPVs) to store hydrogen, especially at high pressures, is gaining more popularity due to their lightweight design and high storage density, offering significant economic advantages. However, the presence of material defects or fatigue can lead to critical failures, requiring an innovative and robust approach to ensure safe operation and system integrity. Developing a continuous structural health monitoring (SHM) system for COPVs can provide comprehensive real-time information about their condition, facilitating a shift away from periodic inspections. This study scrutinizes the behavior of guided waves (GWs) within COPVs to design a sensor network for damage detection and localization. First, the dispersive and multimodal propagation behavior of GWs is experimentally investigated. Subsequently, important parameters for the network design are derived and finally a sensor network consisting of 15 piezoelectric transducers is designed to cover the entire cylindrical area. The effectiveness is then evaluated experimentally by placing artificial defects on the surface of the COPV. The multi-layered dataset of GW signals was analyzed using both commonly used ultrasonic features (e.g., amplitude, frequency, time of flight) as well as statistical features (kurtosis, skewness, variance, etc.). These features were utilized to compute a damage index, and the effectiveness of the detection performance was assessed using receiver operating characteristic curves. It can be seen that some features are more sensitive and robust under varying experimental conditions. The results show that ultrasonic GW SHM system is a promising solution for damage detection and localization in COPVs.

Structural edge damage detection based on wavelet transform and immune genetic algorithm

Structural edge damage detection based on wavelet transform and immune genetic algorithm

Real-time dynamic scale-aware fusion detection network: take road damage detection as an example

Real-time dynamic scale-aware fusion detection network: take road damage detection as an example

Detection of Surface Damage on Steel Wire Ropes Based on Improved U-Net

Detection of Surface Damage on Steel Wire Ropes Based on Improved U-Net

A Review Article: Mitigation Strategies for Seismic Damage in Structures

This in-depth review article provides a comprehensive analysis of mitigation strategies for seismic damage in structures, focusing on the importance of understanding seismic properties and implementing advanced analysis techniques. The review underscores the critical role of structural properties, including stiffness, strength, and ductility, in designing earthquake-resistant structures. By exploring a range of seismic analysis methods, such as force-based, displacement-based, and numerical approaches, the review highlights the significance of a holistic approach in assessing structural response to seismic events. Moreover, the review delves into innovative methods like subspace system identification for damage detection and statistical analysis of seismic sequences, offering promising avenues for future research. The adoption of passive energy dissipation systems and base isolation techniques emerges as key strategies in reducing seismic damage and enhancing structural resilience. Additionally, the review discusses the potential of machine learning techniques in predicting seismicity rates and improving risk management in seismic-prone areas. By combining traditional and advanced methods, this review sets the stage for further developments in seismic damage mitigation strategies. The integration of technological expertise, scientific aptitude, and interdisciplinary approaches is highlighted as essential in ensuring the built environment adapts to unforeseeable natural forces. Ultimately, this review provides a foundational resource for academics, policymakers, and practitioners in the field of earthquake mitigation and structural safety.

EADG-YOLO: road damage detection by improving of YOLOv8n

EADG-YOLO: road damage detection by improving of YOLOv8n

Deep neural network for electromechanical impedance analysis and damage detection of laminated rubber bearings

Abstract Environmental factors and loads cause potential small cracks in the rubber layers of laminated bearings. Conventional methods for identifying structural damage based on global vibration characteristics are ineffective, and current nondestructive structural damage detection methods based on Piezoelectric ceramic lead Zirconate Titanate (PZT) cannot fully capture deep damage-sensitive features from PZT signals. This study proposes a novel deep learning approach using a convolutional neural network with residual connections to conduct raw electromechanical admittance analysis and automatically extract damage-sensitive features to identify minor cracks in laminated rubber bearings. Numerical models are used to generate a sufficient amount of PZT data under various damage conditions for training the deep neural network, which overcomes the sparsity of damage states in actual laminated rubber bearings. A transfer learning method based on a model fine-tuning strategy was adopted to address the feature differences between the numerical models and actual rubber bearing structure. The fine-tuned model could effectively identify the damage location of actual laminated rubber bearings. The proposed method does not require a complex data preprocessing process when analyzing PZT signals and has a relatively high accuracy in damage localization. The promising results from this work offer a potential paradigm for data-driven PZT signal analysis, which is an appealing approach for identifying minor damage in real-life laminated rubber bearings.

Automating container damage detection with the YOLO-NAS deep learning model.

Ensuring the integrity of shipping containers is crucial for maintaining product quality, logistics efficiency, and safety in the global supply chain. Damaged containers can lead to significant economic losses, delays, and safety hazards. Traditionally, container inspections have been manual, which are labor-intensive, time-consuming, and error-prone, especially in busy port environments. This study introduces an automated solution using the YOLO-NAS model, a cutting-edge deep learning architecture known for its adaptability, computational efficiency, and high accuracy in object detection tasks. Our research is among the first to apply YOLO-NAS to container damage detection, addressing the complex conditions of seaports and optimizing for high-speed, high-accuracy performance essential for port logistics. Our method showcases YOLO-NAS's superior efficacy in detecting container damage, achieving a mean average precision (mAP) of 91.2%, a precision rate of 92.4%, and a recall of 84.1%. Comparative analyses indicate that YOLO-NAS consistently outperforms other leading models like YOLOv8 and Roboflow 3.0, which showed lower mAP, precision, and recall values under similar conditions. Additionally, while models such as Fmask-RCNN and MobileNetV2 exhibit high training accuracy, they lack the real-time assessment capabilities critical for port applications, making YOLO-NAS a more suitable choice. The successful integration of YOLO-NAS for automated container damage detection has significant implications for the logistics industry, enhancing port operations with reliable, real-time inspection solutions that can seamlessly integrate into predictive maintenance and monitoring systems. This approach reduces operational costs, improves safety, and lessens the reliance on manual inspections, contributing to the development of "smart ports" with higher efficiency and sustainability in container management.

Shear horizontal guided wave propagation analysis in variable cross-section stiffener and damage detection in stiffened panel

Shear horizontal guided wave propagation analysis in variable cross-section stiffener and damage detection in stiffened panel

Real-time identification of foundation damage in high-Pile Wharves: Nonlinear feature change point analysis in dynamic characteristics under wave excitation

Waves are a common excitation form for wharves. The responses of pile foundation in high-pile wharves under such excitation exhibit pronounced nonlinear and non-stationary characteristics, compromising the robustness of damage detection methods. To address this challenge, this paper first introduces a method using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and spectral feature analysis, aimed at extracting the overall characteristic sub-signals to resolve multi-type signal aliasing. Secondly, the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is employed to isolate the nonlinear dynamic features of the response under wave excitation. Thirdly, the Weighted Residual Cumulative Sum (WRCUSUM) method is developed to monitor changes in response characteristics online, thereby enabling the identification of damage occurrence and its timing. Further, the efficacy of the proposed method has been validated through high-pile wharf experiments conducted at various damage levels. This approach not only offers a novel solution for online damage detection under wave excitation but also introduces a new strategy for non-destructive testing across offshore engineering applications.

On-line detection of 3D non-uniform erosion-corrosion damage in a reducer using an innovated ring-pair-electrical-resistance-sensor array

On-line detection of 3D non-uniform erosion-corrosion damage in a reducer using an innovated ring-pair-electrical-resistance-sensor array

Early Detection of Renal Damage Using Kidney Injury Molecule -1 (KIM-1) In Dogs Associated with Dehydration

Early Detection of Renal Damage Using Kidney Injury Molecule -1 (KIM-1) In Dogs Associated with Dehydration

Cross-domain damage detection through partial conditional adversarial domain adaptation

Cross-domain damage detection through partial conditional adversarial domain adaptation

Single-molecule toxicogenomics: Optical genome mapping of DNA-damage in nanochannel arrays.

Quantitative genomic mapping of DNA damage may provide insights into the underlying mechanisms of damage and repair. Sequencing based approaches are bound to the limitations of PCR amplification bias and read length which hamper both the accurate quantitation of damage events and the ability to map them to structurally complex genomic regions. Optical Genome mapping in arrays of parallel nanochannels allows physical extension and genetic profiling of millions of long genomic DNA fragments, and has matured to clinical utility for characterization of complex structural aberrations in cancer genomes. Here we present a new mapping modality, Repair-Assisted Damage Detection - Optical Genome Mapping (RADD-OGM), a method for single-molecule level mapping of DNA damage on a genome-wide scale. Leveraging ultra-long reads to assemble the complex structure of a sarcoma cell-line genome, we mapped the genomic distribution of oxidative DNA damage, identifying regions more susceptible to DNA oxidation. We also investigated DNA repair by allowing cells to repair chemically induced DNA damage, pinpointing locations of concentrated repair activity, and highlighting variations in repair efficiency. Our results showcase the potential of the method for toxicogenomic studies, mapping the effect of DNA damaging agents such as drugs and radiation, as well as following specific DNA repair pathways by selective induction of DNA damage. The facile integration with optical genome mapping enables performing such analyses even in highly rearranged genomes such as those common in many cancers, a challenging task for sequencing-based approaches.

Debonding damage detection in CFRP-reinforced steel structures using scanning probabilistic imaging method improved by ultrasonic guided-wave transfer function

Debonding damage detection in CFRP-reinforced steel structures using scanning probabilistic imaging method improved by ultrasonic guided-wave transfer function

Structural damage detection for a small population of nominally equal beams using PSO-optimized Convolutional Neural Networks

Structural damage detection for a small population of nominally equal beams using PSO-optimized Convolutional Neural Networks