This paper presents a feasibility of detecting anomalous displacements on bridges where collapses have occurred using C-band satellite synthetic aperture radar (SAR). Recently, the number of aging bridges due to damage and corrosion has been increased in many countries. Although there are various maintenance and management systems, the authors have focused on satellite SAR, one of the remote sensing technologies, to establish a wide-area multiple bridge monitoring technology. In a previous study, the authors analyzed high-resolution X-band interferometric SAR (InSAR) data acquired by the Italian satellite COSMO-SkyMed during the two years before the accident for the MUSOTA water pipe bridge in Japan, which collapsed on October 2021. As a result, it was shown that there was an anomalous displacement about one year before the accident, which seemed to be a sign of the collapse. On the other hand, the utilization of Sentinel-1 (C-band) data, which has lower resolution than X-band data, but is freely available, covers a wide area, and has dense time-series SAR images, would be beneficial for the practical application of this technology. In this study, InSAR data acquired by Sentinel-1 for the MUSOTA water pipe bridge from 2016 to 2022 are analyzed. Then, we compare the line-of-sight (LOS) displacement anomaly before the collapse with the results analyzed by the X-band satellite. As a result of statistical analysis of the displacement between the collapsed span and the adjacent spans with similar structural type assumed to be intact, it is shown that Sentinel-1 also captured differences which seemed to be the signs of the collapse about one year before the accident. This result supports the analysis of the X-band satellite data and shows potential for utilization in bridge anomaly detection systems, even for low-resolution C-band data.
本文介绍了利用 C 波段卫星合成孔径雷达（SAR）探测发生坍塌的桥梁的异常位移的可行性。近年来，许多国家因损坏和腐蚀而老化的桥梁数量不断增加。虽然有各种维护和管理系统，但作者还是把重点放在了遥感技术之一的卫星合成孔径雷达上，以建立大范围的多桥梁监测技术。在之前的一项研究中，作者分析了意大利卫星 COSMO-SkyMed 在 2021 年 10 月坍塌的日本 MUSOTA 水管桥事故发生前两年获取的高分辨率 X 波段干涉合成孔径雷达（InSAR）数据。结果表明，在事故发生前一年左右出现了异常位移，这似乎是坍塌的征兆。另一方面，哨兵-1（C 波段）数据的分辨率低于 X 波段数据，但可免费获取，覆盖面积广，且具有密集的时间序列合成孔径雷达图像，利用这些数据将有利于该技术的实际应用。本研究分析了 Sentinel-1 获取的 2016 年至 2022 年 MUSOTA 水管桥 InSAR 数据。然后，我们将坍塌前的视线（LOS）位移异常与 X 波段卫星分析的结果进行比较。通过统计分析坍塌跨度与相邻跨度之间的位移（假定结构类型相似且完好无损），结果表明哨兵-1 号卫星也捕捉到了事故发生前一年左右的差异，这些差异似乎是坍塌的征兆。这一结果支持了对 X 波段卫星数据的分析，并显示了在桥梁异常检测系统中使用的潜力，即使是低分辨率的 C 波段数据也是如此。
{"title":"Comparison of anomalous displacement detectability of bridges between X and C band InSAR data: Case study on a collapse of water pipe bridge","authors":"Kosuke Kinoshita, Yukihiro Yano, Takahiro Kumura","doi":"10.58286/29761","DOIUrl":"https://doi.org/10.58286/29761","url":null,"abstract":"\u0000This paper presents a feasibility of detecting anomalous displacements on bridges where collapses have occurred using C-band satellite synthetic aperture radar (SAR). Recently, the number of aging bridges due to damage and corrosion has been increased in many countries. Although there are various maintenance and management systems, the authors have focused on satellite SAR, one of the remote sensing technologies, to establish a wide-area multiple bridge monitoring technology. In a previous study, the authors analyzed high-resolution X-band interferometric SAR (InSAR) data acquired by the Italian satellite COSMO-SkyMed during the two years before the accident for the MUSOTA water pipe bridge in Japan, which collapsed on October 2021. As a result, it was shown that there was an anomalous displacement about one year before the accident, which seemed to be a sign of the collapse. On the other hand, the utilization of Sentinel-1 (C-band) data, which has lower resolution than X-band data, but is freely available, covers a wide area, and has dense time-series SAR images, would be beneficial for the practical application of this technology. In this study, InSAR data acquired by Sentinel-1 for the MUSOTA water pipe bridge from 2016 to 2022 are analyzed. Then, we compare the line-of-sight (LOS) displacement anomaly before the collapse with the results analyzed by the X-band satellite. As a result of statistical analysis of the displacement between the collapsed span and the adjacent spans with similar structural type assumed to be intact, it is shown that Sentinel-1 also captured differences which seemed to be the signs of the collapse about one year before the accident. This result supports the analysis of the X-band satellite data and shows potential for utilization in bridge anomaly detection systems, even for low-resolution C-band data.\u0000","PeriodicalId":294137,"journal":{"name":"e-Journal of Nondestructive Testing","volume":"41 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141704770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Railway bridges have a long service life - in Germany, for example, the average is 122 years. During this time, vehicles are constantly evolving, leading to innovative axle configurations and higher speeds. This leads to loads for which the bridges were not originally designed, which can lead to resonant structural responses. A key parameter for calculating and evaluating the dynamic behaviour of bridge structures, is the resonance frequency. Due to the large number of bridges in the network, conventional methods using sensors on the structure would be very laborious. A cost-effective alternative would be drive-by monitoring, using sensors on a passing train. In railway bridge contexts, drive-by monitoring to identify frequencies faces challenges from short spans and high speeds, leading to brief contact periods. The brief periods cause a low resolution of the signals in the frequency domain. An approach to overcome this issue is the use of resonance curve-based drive-by monitoring. The term resonance curve describes the maximum structural responses as a function of speed. In conjunction with the known axle configuration of the train, the resonance frequency can be determined from the resonance curve. In this paper, data from two field tests are analysed, in which synchronised acceleration measurements were conducted on the main girders of the bridges and the axle boxes of the trains. An ICE 4 was used on a bridge with a 19.5-metre span, and an ICE TD on a bridge spanning 16.4 metres. The analyses show that frequency identification using indirect resonance curves is feasible under real operating conditions. Thus, this approach enables the determination of a key parameter essential for calibrating structural models. Furthermore, this method enables the avoidance of resonance crossings by speed adaptation, either by acceleration or deceleration. This strategy has the potential to significantly increase the service life of bridges.
{"title":"Frequency Identification using Resonance Curve-Based Drive-by Monitoring: Field Validation","authors":"S. Lorenzen, M. Rupp, C. Hübler","doi":"10.58286/29722","DOIUrl":"https://doi.org/10.58286/29722","url":null,"abstract":"\u0000Railway bridges have a long service life - in Germany, for example, the average is 122 years. During this time, vehicles are constantly evolving, leading to innovative axle configurations and higher speeds. This leads to loads for which the bridges were not originally designed, which can lead to resonant structural responses.\u0000A key parameter for calculating and evaluating the dynamic behaviour of bridge structures, is the resonance frequency. Due to the large number of bridges in the network, conventional methods using sensors on the structure would be very laborious. A cost-effective alternative would be drive-by monitoring, using sensors on a passing train.\u0000In railway bridge contexts, drive-by monitoring to identify frequencies faces challenges from short spans and high speeds, leading to brief contact periods. The brief periods cause a low resolution of the signals in the frequency domain. An approach to overcome this issue is the use of resonance curve-based drive-by monitoring. The term resonance curve describes the maximum structural responses as a function of speed. In conjunction with the known axle configuration of the train, the resonance frequency can be determined from the resonance curve.\u0000In this paper, data from two field tests are analysed, in which synchronised acceleration measurements were conducted on the main girders of the bridges and the axle boxes of the trains. An ICE 4 was used on a bridge with a 19.5-metre span, and an ICE TD on a bridge spanning 16.4 metres.\u0000The analyses show that frequency identification using indirect resonance curves is feasible under real operating conditions. Thus, this approach enables the determination of a key parameter essential for calibrating structural models. Furthermore, this method enables the avoidance of resonance crossings by speed adaptation, either by acceleration or deceleration. This strategy has the potential to significantly increase the service life of bridges.\u0000\u0000","PeriodicalId":294137,"journal":{"name":"e-Journal of Nondestructive Testing","volume":"132 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141714197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The identification of material parameters occurring in material models is essential for structural health monitoring. Due to chemical and physical processes, building structures and materials age during their service life. This, in turn, leads to a deterioration in both the reliability and quality of the structures. Knowing the current condition of the building structures can help prevent disasters and extend service life. We developed a physics-informed neural network (PINN) [1] for the calibration of the linear-elastic material model from full-field displacement data measured by digital image correlation. In an offline-phase, the PINN is trained to learn a parameterized solution of the underlying parametric partial differential equation without the need for training data [2]. We demonstrate the ability of the parametric PINN to act as a surrogate in a least-squares based material model calibration. In order to quantify the uncertainty, we further use the parametric PINN with Markov Chain Monte Carlo based Bayesian inference. Even with artificially noisy data, the calibration produces good results for reasonable material parameter ranges. Especially in sampling based methods, parametric PINNs have the advantage that model evaluation is very cheap compared to, e.g., the Finite Element Method. Thus, information on the material condition can be provided in near real-time in the online-phase. Moreover, PINNs use a continuous ansatz and thereby avoid the need to interpolate sensor locations to the simulation domain. In our ongoing work, we plan to apply the parametric PINN to more complex material models, such as those for elasto-plastic materials. We also investigate the extension of the proposed method to more complex geometries. [1] M. Raissi et al.: Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, Journal of Computational Physics 378, 686-707 (2019). [2] A. Beltrán-Pulido et al.: Physics-Informed Neural Networks for Solving Parametric Magnetostatic Problems, IEEE Transactions on Energy Conversion 37(4), 2678-2689 (2022).
确定材料模型中的材料参数对结构健康监测至关重要。由于化学和物理过程的影响，建筑结构和材料在其使用寿命期间会发生老化。这反过来又会导致结构的可靠性和质量下降。了解建筑结构的当前状况有助于预防灾害和延长使用寿命。我们开发了一种物理信息神经网络（PINN）[1]，用于根据数字图像相关测量的全场位移数据校准线性弹性材料模型。在离线阶段，对 PINN 进行训练，以学习底层参数偏微分方程的参数化解决方案，而无需训练数据[2]。我们展示了参数化 PINN 在基于最小二乘法的材料模型校准中充当代理的能力。为了量化不确定性，我们进一步将参数 PINN 与基于 Markov Chain Monte Carlo 的贝叶斯推理相结合。即使使用人为噪声数据，校准也能在合理的材料参数范围内产生良好的结果。特别是在基于采样的方法中，参数化 PINN 的优势在于，与有限元法等方法相比，模型评估的成本非常低。因此，可以在在线阶段提供近乎实时的材料状况信息。此外，PINN 使用连续反演，因此无需将传感器位置插值到模拟域。在目前的工作中，我们计划将参数化 PINN 应用于更复杂的材料模型，如弹塑性材料模型。我们还将研究如何将提议的方法扩展到更复杂的几何形状。[1] M. Raissi 等人：物理信息神经网络：用于解决涉及非线性偏微分方程的正演和反演问题的深度学习框架，《计算物理学杂志》378 期，686-707（2019 年）。[2] A. Beltrán-Pulido et al：Physics-Informed Neural Networks for Solving Parametric Magnetostatic Problems, IEEE Transactions on Energy Conversion 37(4), 2678-2689 (2022).
{"title":"Parametric Neural Networks as Full-Field Surrogates for Material Model Calibration","authors":"Ganesh Shivalingappa, D. Anton, Henning Wessels","doi":"10.58286/29583","DOIUrl":"https://doi.org/10.58286/29583","url":null,"abstract":"\u0000The identification of material parameters occurring in material models is essential for structural health monitoring. Due to chemical and physical processes, building structures and materials age during their service life. This, in turn, leads to a deterioration in both the reliability and quality of the structures. Knowing the current condition of the building structures can help prevent disasters and extend service life. \u0000\u0000We developed a physics-informed neural network (PINN) [1] for the calibration of the linear-elastic material model from full-field displacement data measured by digital image correlation. In an offline-phase, the PINN is trained to learn a parameterized solution of the underlying parametric partial differential equation without the need for training data [2]. We demonstrate the ability of the parametric PINN to act as a surrogate in a least-squares based material model calibration. In order to quantify the uncertainty, we further use the parametric PINN with Markov Chain Monte Carlo based Bayesian inference. Even with artificially noisy data, the calibration produces good results for reasonable material parameter ranges. Especially in sampling based methods, parametric PINNs have the advantage that model evaluation is very cheap compared to, e.g., the Finite Element Method. Thus, information on the material condition can be provided in near real-time in the online-phase. Moreover, PINNs use a continuous ansatz and thereby avoid the need to interpolate sensor locations to the simulation domain. \u0000\u0000In our ongoing work, we plan to apply the parametric PINN to more complex material models, such as those for elasto-plastic materials. We also investigate the extension of the proposed method to more complex geometries. \u0000\u0000\u0000\u0000[1] M. Raissi et al.: Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, Journal of Computational Physics 378, 686-707 (2019). \u0000\u0000[2] A. Beltrán-Pulido et al.: Physics-Informed Neural Networks for Solving Parametric Magnetostatic Problems, IEEE Transactions on Energy Conversion 37(4), 2678-2689 (2022).\u0000","PeriodicalId":294137,"journal":{"name":"e-Journal of Nondestructive Testing","volume":"164 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141693291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automated monitoring systems in structural health monitoring (SHM) using finite element models can measure system responses of the structure and provide information about behavior and damage scenarios. To describe the reactions and damages of the physical structure, the parameters of the FE-model have to be adjusted accordingly. A common approach therefore is model-updating, an iterative modification of unknown parameters of a finite element model by adapting the response of the model to the measurement results of the physical bridge. The adapted model can be used for assessments and predictions of the structure and provides accurate structural properties to a digital twin. The proposed monitoring concept includes damage detection, based on nonlinear model-updating using Artificial Intelligence (AI) methods and is applied to a prestressed, reinforced concrete bridge based on the observation of static response. For a practical application in long-term SHM, system properties and load values are identified simultaneously by using optimization methods based on Evolutionary Algorithms (EA). Due to the high dimensionality and complexity of the optimization results, Principal Component Analysis (PCA) including autoencoder networks for dimensionality reduction are required to evaluate and visualize the identified structural properties for their reliability. Based on physical 3D nonlinear finite element sensitivity analysis, the influence of damage scenarios and load situations on the bearing behavior of the bridge will be analyzed. The generated data and the results are presented and serve as the basis for the reliability analysis and evaluating the proposed concept. Furthermore, by comparing the measurements of the parameterized investigated models, conclusions about the behavior of the structure and the sensor placement can be obtained.
{"title":"Sensitivity analysis of model parameters in a nonlinear model-updating approach for prestressed concrete bridges","authors":"Bjarne Sprenger, Martina Schnellenbach-Held","doi":"10.58286/29584","DOIUrl":"https://doi.org/10.58286/29584","url":null,"abstract":"\u0000Automated monitoring systems in structural health monitoring (SHM) using finite element models can measure system responses of the structure and provide information about behavior and damage scenarios. To describe the reactions and damages of the physical structure, the parameters of the FE-model have to be adjusted accordingly. A common approach therefore is model-updating, an iterative modification of unknown parameters of a finite element model by adapting the response of the model to the measurement results of the physical bridge. The adapted model can be used for assessments and predictions of the structure and provides accurate structural properties to a digital twin. The proposed monitoring concept includes damage detection, based on nonlinear model-updating using Artificial Intelligence (AI) methods and is applied to a prestressed, reinforced concrete bridge based on the observation of static response. For a practical application in long-term SHM, system properties and load values are identified simultaneously by using optimization methods based on Evolutionary Algorithms (EA). Due to the high dimensionality and complexity of the optimization results, Principal Component Analysis (PCA) including autoencoder networks for dimensionality reduction are required to evaluate and visualize the identified structural properties for their reliability. Based on physical 3D nonlinear finite element sensitivity analysis, the influence of damage scenarios and load situations on the bearing behavior of the bridge will be analyzed. The generated data and the results are presented and serve as the basis for the reliability analysis and evaluating the proposed concept. Furthermore, by comparing the measurements of the parameterized investigated models, conclusions about the behavior of the structure and the sensor placement can be obtained. \u0000","PeriodicalId":294137,"journal":{"name":"e-Journal of Nondestructive Testing","volume":"13 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141693988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reem Yassine, Samir Mustapha, Mohammad Ali Fakih, Paweł H. Malinowski
Timber structures have been widely used in critical engineering applications throughout history. The need for Non-Destructive Testing of timbers has arisen, and ultrasonic-guided waves (GWs) have proved their efficacy in studying structural integrity. The effect of the grain direction in orthotropic timber structures on the propagation characteristics of GWs is not well understood, especially in the presence of moisture. This study aims to comprehend the effects of timber-grain orientations and the moisture content (MC) on dominant GW modes, specifically, on the anti-symmetric A0 mode. The study is based on samples extracted from a Western White Pine utility pole, in three different orientations including longitudinal, tangential, and radial. The MC is introduced using a desiccator with a saturated salt solution. The studied MC conditions included dry, ambient MC, and high MCs varying from 10% to 27%. The GWs were excited using piezoelectric actuators and sensed with a Scanning Laser Doppler Vibrometer with Free-Free boundary condition. Phase velocities were obtained using the frequency-wavenumber plots of the measured line scans and were compared to the analytical curves for mode analysis. The results showed that the experimental A0-mode phase velocities match well with those generated analytically. The difference was negligible for longitudinally oriented specimens with a percentage difference ranging from 0.28% to 5.34%, as the MC varied. The phase velocity decreased by about 7% as the MC increased from 0% to 27%. A noticeable difference in the phase velocity was measured with different orientations; at dry conditions, the A0 propagated at 958 m/s in the longitudinal specimens, while it decreased in the tangential specimens to about 760 m/s, and had the lowest phase velocity in the radial direction reaching 433 m/s. The results show a high sensitivity of the propagating GWs to the direction of the fibers and the MC present in the timber.
{"title":"Non-Contact Laser Doppler Vibrometer for Characterization of Guided Waves in Timber Structures with Varying Moisture Content and Grain Orientations","authors":"Reem Yassine, Samir Mustapha, Mohammad Ali Fakih, Paweł H. Malinowski","doi":"10.58286/29680","DOIUrl":"https://doi.org/10.58286/29680","url":null,"abstract":"\u0000Timber structures have been widely used in critical engineering applications throughout history. The need for Non-Destructive Testing of timbers has arisen, and ultrasonic-guided waves (GWs) have proved their efficacy in studying structural integrity. The effect of the grain direction in orthotropic timber structures on the propagation characteristics of GWs is not well understood, especially in the presence of moisture. This study aims to comprehend the effects of timber-grain orientations and the moisture content (MC) on dominant GW modes, specifically, on the anti-symmetric A0 mode.\u0000The study is based on samples extracted from a Western White Pine utility pole, in three different orientations including longitudinal, tangential, and radial. The MC is introduced using a desiccator with a saturated salt solution. The studied MC conditions included dry, ambient MC, and high MCs varying from 10% to 27%. The GWs were excited using piezoelectric actuators and sensed with a Scanning Laser Doppler Vibrometer with Free-Free boundary condition. Phase velocities were obtained using the frequency-wavenumber plots of the measured line scans and were compared to the analytical curves for mode analysis.\u0000The results showed that the experimental A0-mode phase velocities match well with those generated analytically. The difference was negligible for longitudinally oriented specimens with a percentage difference ranging from 0.28% to 5.34%, as the MC varied. The phase velocity decreased by about 7% as the MC increased from 0% to 27%. A noticeable difference in the phase velocity was measured with different orientations; at dry conditions, the A0 propagated at 958 m/s in the longitudinal specimens, while it decreased in the tangential specimens to about 760 m/s, and had the lowest phase velocity in the radial direction reaching 433 m/s.\u0000The results show a high sensitivity of the propagating GWs to the direction of the fibers and the MC present in the timber. \u0000\u0000","PeriodicalId":294137,"journal":{"name":"e-Journal of Nondestructive Testing","volume":"1 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141690877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents an active thermographic measurement-based inspection tech-nique for detecting weld defects in the battery cap region of cylindrical secondary batteries. As the proliferation of electric vehicles continues, safety issues related to secondary batteries have become prominent. In this study, a defect detection algorithm was developed by aligning visual images and active thermographic im-ages of the cylindrical secondary battery cap region. Lock-in amplitude images during the heating-cooling process using a laser were extracted to measure the difference in temperature patterns arising from internal structural variations. Ad-ditionally, to minimize the influence of surface conditions and temperature varia-tions other than laser-induced, a 10Hz periodic heating was employed, and ther-mal magnification in the heating frequency was achieved through data processing. This approach allowed the measurement of temperature changes induced by laser heating and cooling while minimizing the impact of surface conditions and tem-perature variations unrelated to the laser. A dataset of 1000 specimens was con-structed by aligning thermographic data with vision images acquired through this method. defective samples. Using this thermographic-vision alignment dataset, a defect detection algorithm was trained to achieve a more accurate and faster in-spection performance compared to existing non-destructive testing technologies.
{"title":"Weld Defect Inspection of Cylindrical Secondary Batteries Based on Active Thermographic Measurement with Thermal Magnification","authors":"J. Park, S. Shin, Hoon Sohn","doi":"10.58286/29738","DOIUrl":"https://doi.org/10.58286/29738","url":null,"abstract":"\u0000This paper presents an active thermographic measurement-based inspection tech-nique for detecting weld defects in the battery cap region of cylindrical secondary batteries. As the proliferation of electric vehicles continues, safety issues related to secondary batteries have become prominent. In this study, a defect detection algorithm was developed by aligning visual images and active thermographic im-ages of the cylindrical secondary battery cap region. Lock-in amplitude images during the heating-cooling process using a laser were extracted to measure the difference in temperature patterns arising from internal structural variations. Ad-ditionally, to minimize the influence of surface conditions and temperature varia-tions other than laser-induced, a 10Hz periodic heating was employed, and ther-mal magnification in the heating frequency was achieved through data processing. This approach allowed the measurement of temperature changes induced by laser heating and cooling while minimizing the impact of surface conditions and tem-perature variations unrelated to the laser. A dataset of 1000 specimens was con-structed by aligning thermographic data with vision images acquired through this method. defective samples. Using this thermographic-vision alignment dataset, a defect detection algorithm was trained to achieve a more accurate and faster in-spection performance compared to existing non-destructive testing technologies.\u0000","PeriodicalId":294137,"journal":{"name":"e-Journal of Nondestructive Testing","volume":"20 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141715516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Over the last few years, the management of multi-asset infrastructure over wide regions has experienced a significant evolution due to novel technologies or accessibility to cheaper sensors combined with the Internet of Things (IoT). Interferometric Synthetic Aperture Radar (InSAR) data has proven to be the most cost-effective technology to meet users’ requests. However, InSAR’s large volume of data presents challenges for end-users. Data aggregation and visualisation are needed to streamline operations and information transfer between teams. This proposed methodology seeks to address the challenges associated with InSAR data by integrating InSAR observations with the digital equivalent of existing structures in a repeatable and scalable manner. To achieve this, deformation profiles are generated adaptively, considering the asset’s characteristics, such as width and orientation, as well as the satellite acquisition geometry. In parallel, a two-dimensional approach is followed to associate InSAR information with each structure element and thus organise the information spatially. It should be noted that, even when using high-resolution radar data, it is sometimes challenging to associate radar returns with specific asset segments due to the complex interaction of microwaves with particular targets. Consequently, a key feature of this methodology is to classify InSAR data according to a precise geocoding of each scatterer, including an accurate estimation of its elevation. This methodology makes it possible to analyse many different types of infrastructure, which can be time-consuming for large highway or railway operators. The coverage of the InSAR results on a regional scale, and the semi-automated method gives decision-makers an overview of their structures to send maintenance teams, saving time and costs. This methodology allows for InSAR-derived product analysis, making the results more readable, accessible, and impactful. This type of infrastructure monitoring reflects a new maturity of InSAR in this field, demonstrated in real case studies.
{"title":"Enhancing infrastructure management with InSAR monitoring","authors":"Eric Henrion, Jordi Sanchez, Diana Walter","doi":"10.58286/29808","DOIUrl":"https://doi.org/10.58286/29808","url":null,"abstract":"\u0000Over the last few years, the management of multi-asset infrastructure over wide regions has experienced a significant evolution due to novel technologies or accessibility to cheaper sensors combined with the Internet of Things (IoT). Interferometric Synthetic Aperture Radar (InSAR) data has proven to be the most cost-effective technology to meet users’ requests. However, InSAR’s large volume of data presents challenges for end-users. Data aggregation and visualisation are needed to streamline operations and information transfer between teams.\u0000\u0000This proposed methodology seeks to address the challenges associated with InSAR data by integrating InSAR observations with the digital equivalent of existing structures in a repeatable and scalable manner.\u0000\u0000To achieve this, deformation profiles are generated adaptively, considering the asset’s characteristics, such as width and orientation, as well as the satellite acquisition geometry. In parallel, a two-dimensional approach is followed to associate InSAR information with each structure element and thus organise the information spatially.\u0000\u0000It should be noted that, even when using high-resolution radar data, it is sometimes challenging to associate radar returns with specific asset segments due to the complex interaction of microwaves with particular targets. Consequently, a key feature of this methodology is to classify InSAR data according to a precise geocoding of each scatterer, including an accurate estimation of its elevation.\u0000\u0000This methodology makes it possible to analyse many different types of infrastructure, which can be time-consuming for large highway or railway operators. The coverage of the InSAR results on a regional scale, and the semi-automated method gives decision-makers an overview of their structures to send maintenance teams, saving time and costs.\u0000\u0000This methodology allows for InSAR-derived product analysis, making the results more readable, accessible, and impactful. This type of infrastructure monitoring reflects a new maturity of InSAR in this field, demonstrated in real case studies.\u0000\u0000\u0000","PeriodicalId":294137,"journal":{"name":"e-Journal of Nondestructive Testing","volume":"64 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141696293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sam Reid, Alasdair Logan, Euan Miller, David Garcia Cava
In the effort to increase sustainability and help drive the energy sector to support oil and gas decarbonisation, the importance of identifying and characterising the current state of assets is becoming ever more crucial for operators, especially with regards to life extension and repurposing. Structural Health Monitoring (SHM) offers a promising and forward-thinking methodology for tracking asset integrity. In particular, Operational Modal Analysis (OMA) presents a proven, output only method for structure characterisation and modal tracking. However, Environmental and Operational Variations (EOV) can influence the modal properties of the structure making modal parameter estimation and tracking less reliable in the short- and long-term. This study employs an Automated Operational Modal Analysis (AOMA) methodology based on the combination of stochastic subspace identification and natural frequency histogram bin analysis for robust parameter extraction. The methodology is implemented in a complex of three bridge linked offshore platforms. Structural complexity, operational loading and significant modal couplings require sophisticated analysis. The preliminary results show discontinuity of the operational modes over a 6-month long monitoring period. The findings demonstrate the need of further analysis to understand the time-variant parameters which determine structural response across the asset lifetime.
{"title":"Operational modal characterisation for long-term monitoring in offshore structures","authors":"Sam Reid, Alasdair Logan, Euan Miller, David Garcia Cava","doi":"10.58286/29642","DOIUrl":"https://doi.org/10.58286/29642","url":null,"abstract":"\u0000In the effort to increase sustainability and help drive the energy sector to support oil and gas decarbonisation, the importance of identifying and characterising the current state of assets is becoming ever more crucial for operators, especially with regards to life extension and repurposing. Structural Health Monitoring (SHM) offers a promising and forward-thinking methodology for tracking asset integrity. In particular, Operational Modal Analysis (OMA) presents a proven, output only method for structure characterisation and modal tracking. However, Environmental and Operational Variations (EOV) can influence the modal properties of the structure making modal parameter estimation and tracking less reliable in the short- and long-term. This study employs an Automated Operational Modal Analysis (AOMA) methodology based on the combination of stochastic subspace identification and natural frequency histogram bin analysis for robust parameter extraction. The methodology is implemented in a complex of three bridge linked offshore platforms. Structural complexity, operational loading and significant modal couplings require sophisticated analysis. The preliminary results show discontinuity of the operational modes over a 6-month long monitoring period. The findings demonstrate the need of further analysis to understand the time-variant parameters which determine structural response across the asset lifetime.\u0000","PeriodicalId":294137,"journal":{"name":"e-Journal of Nondestructive Testing","volume":"49 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141697802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Roloff, Jörn Froböse, C. Hühne, Michael Sinapius
Damage in thin-walled structures can be detected by guided ultrasonic wave (GUW) based structural health monitoring systems. The application of phased arrays enables the scanning of large-scale structures from a single position. However, physical wave focusing requires a lot of effort. This can be significantly reduced if frequency response functions (FRFs) are used. They enable the calculation of virtual response signals for virtual focusing on any position. Although this technique offers an energy-efficient and fast possibility of damage detection, it has the disadvantage of artefacts that occur due to the multimodal nature of GUW, as damage detection is generally designed for single-mode signals. These artefacts are often reduced by mode-selective excitation/sensing or by subtracting a baseline measurement. This work presents a concept to combine an existing FRF-based damage detection algorithm and a previously presented method for mode extraction. It enables the extraction of GUW mode components from broadband, temporally sampled, single-input single-output sensor data during signal processing on the basis of the respective dispersion relations. The aim is to reduce artefacts without using mode-selective excitation/sensing or baseline measurements. A finite element simulation of GUW propagation in an isotropic structure is used to demonstrate the advantages and limitations of this approach. The simulations include multimodal and single mode evaluation to point out the added value of performing mode extraction prior to damage detection. It is supposed that the successful extraction of the mode components from the temporally sampled data results in a decreasing amplitude of the occurring artefacts compared to the multimodal case, while the case of mode-selective excitation apparently results in no artefacts. The presented concept of adding mode extraction to damage detection algorithms can lead to an increase in performance of FRF-based phased array systems. Artefacts that would lead to false detection of damage are reduced inherently during signal processing which eliminates the need for mode-selective excitation/sensing or baseline measurements.
{"title":"Reducing Artefacts in FRF-based Damage Detection Using Novel Mode Extraction Approach for Guided Ultrasonic Waves","authors":"T. Roloff, Jörn Froböse, C. Hühne, Michael Sinapius","doi":"10.58286/29821","DOIUrl":"https://doi.org/10.58286/29821","url":null,"abstract":"\u0000Damage in thin-walled structures can be detected by guided ultrasonic wave (GUW) based structural health monitoring systems. The application of phased arrays enables the scanning of large-scale structures from a single position. However, physical wave focusing requires a lot of effort. This can be significantly reduced if frequency response functions (FRFs) are used. They enable the calculation of virtual response signals for virtual focusing on any position. Although this technique offers an energy-efficient and fast possibility of damage detection, it has the disadvantage of artefacts that occur due to the multimodal nature of GUW, as damage detection is generally designed for single-mode signals. These artefacts are often reduced by mode-selective excitation/sensing or by subtracting a baseline measurement.\u0000\u0000This work presents a concept to combine an existing FRF-based damage detection algorithm and a previously presented method for mode extraction. It enables the extraction of GUW mode components from broadband, temporally sampled, single-input single-output sensor data during signal processing on the basis of the respective dispersion relations. The aim is to reduce artefacts without using mode-selective excitation/sensing or baseline measurements. A finite element simulation of GUW propagation in an isotropic structure is used to demonstrate the advantages and limitations of this approach. The simulations include multimodal and single mode evaluation to point out the added value of performing mode extraction prior to damage detection.\u0000\u0000It is supposed that the successful extraction of the mode components from the temporally sampled data results in a decreasing amplitude of the occurring artefacts compared to the multimodal case, while the case of mode-selective excitation apparently results in no artefacts.\u0000\u0000The presented concept of adding mode extraction to damage detection algorithms can lead to an increase in performance of FRF-based phased array systems. Artefacts that would lead to false detection of damage are reduced inherently during signal processing which eliminates the need for mode-selective excitation/sensing or baseline measurements.\u0000\u0000\u0000","PeriodicalId":294137,"journal":{"name":"e-Journal of Nondestructive Testing","volume":"9 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141689139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thomas Schumacher, N. Bertola, N. Epple, E. Brühwiler, E. Niederleithinger
Combined passive [or acoustic emission (AE)] and active ultrasonic stress (US) wave monitoring has been shown to provide a more holistic picture of ongoing fracture processes, damage progression, as well as slowly occurring aging and degradation mechanisms in concrete structures. Traditionally, different data analysis techniques have been used to analyze the data generated from these two monitoring approaches. For AE data analysis, for instance, signal amplitudes, hit rates, source localization, and b-value analysis have been used to detect and locate cracking. On the other hand, amplitude tracking, magnitude squared coherence (MSC), and coda wave interferometry (CWI) are examples that have been employed for US data analysis. In this presentation, we explore these data analysis techniques and show where their respective applications and limitations might be. After providing an overview of the monitoring approach and the different data analysis techniques, results and observations from select laboratory experiments, as well as an in-service structure are discussed. Finally, suggestions for further work are proposed.
事实证明，将被动[或声学发射（AE）]和主动超声应力（US）波监测结合起来，可以更全面地了解混凝土结构中正在发生的断裂过程、损伤进展以及缓慢发生的老化和退化机制。传统上，人们使用不同的数据分析技术来分析这两种监测方法产生的数据。例如，在 AE 数据分析中，信号振幅、命中率、信号源定位和 b 值分析被用来检测和定位裂缝。另一方面，振幅跟踪、幅度平方相干性 (MSC) 和尾波干涉测量法 (CWI) 是用于 US 数据分析的实例。在本讲座中，我们将探讨这些数据分析技术，并说明其各自的应用领域和局限性。在概述了监测方法和不同的数据分析技术之后，我们讨论了精选的实验室实验结果和观察结果，以及一个在役结构。最后，提出了进一步工作的建议。
{"title":"Combined Passive and Active Ultrasonic Stress Wave Monitoring of Concrete Structures: An Overview of Data Analysis Techniques and Their Applications and Limitations","authors":"Thomas Schumacher, N. Bertola, N. Epple, E. Brühwiler, E. Niederleithinger","doi":"10.58286/29863","DOIUrl":"https://doi.org/10.58286/29863","url":null,"abstract":"\u0000Combined passive [or acoustic emission (AE)] and active ultrasonic stress (US) wave monitoring has been shown to provide a more holistic picture of ongoing fracture processes, damage progression, as well as slowly occurring aging and degradation mechanisms in concrete structures. Traditionally, different data analysis techniques have been used to analyze the data generated from these two monitoring approaches. For AE data analysis, for instance, signal amplitudes, hit rates, source localization, and b-value analysis have been used to detect and locate cracking. On the other hand, amplitude tracking, magnitude squared coherence (MSC), and coda wave interferometry (CWI) are examples that have been employed for US data analysis. In this presentation, we explore these data analysis techniques and show where their respective applications and limitations might be. After providing an overview of the monitoring approach and the different data analysis techniques, results and observations from select laboratory experiments, as well as an in-service structure are discussed. Finally, suggestions for further work are proposed.\u0000","PeriodicalId":294137,"journal":{"name":"e-Journal of Nondestructive Testing","volume":"59 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141689587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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