Liu Guan-si, Ding Ke-qin, Chen Li, Ma Tao, Zhang LI-JING
Roller coaster bears alternating load, and fatigue damage is one of the main ways of its failure, and the fatigue of roller coaster structure and the resulting safety problems are becoming more and more prominent. With the rapid construction of large amusement facilities and the increasingly complex forms of movement, safety accidents caused by their failure occur from time to time. In this paper, according to the practical needs of the roller coaster track structure operation safety, the key failure parts of the roller coaster track structure are analyzed, and the fiber Bragg grating strain monitoring technology is used for real-time online monitoring of the key failure parts; Based on the monitoring stress spectrum data, the statistical counting method and cumulative damage theory are used to diagnose and predict the damage situation of the monitoring points, and the cumulative damage degree and crack initiation life of each monitoring point are given, so as to provide support for enterprises to make maintenance plans, and make the original cumbersome and difficult data acquisition more reliable, safe and convenient, Improve the ability and level of roller coaster monitoring and management.
{"title":"RESEARCH ON MONITORING AND DIAGNOSIS TECHNOLOGY OF ROLLER COASTER TRACK STRESS BASED ON FIBER BRAGG GRATING","authors":"Liu Guan-si, Ding Ke-qin, Chen Li, Ma Tao, Zhang LI-JING","doi":"10.12783/shm2021/36248","DOIUrl":"https://doi.org/10.12783/shm2021/36248","url":null,"abstract":"Roller coaster bears alternating load, and fatigue damage is one of the main ways of its failure, and the fatigue of roller coaster structure and the resulting safety problems are becoming more and more prominent. With the rapid construction of large amusement facilities and the increasingly complex forms of movement, safety accidents caused by their failure occur from time to time. In this paper, according to the practical needs of the roller coaster track structure operation safety, the key failure parts of the roller coaster track structure are analyzed, and the fiber Bragg grating strain monitoring technology is used for real-time online monitoring of the key failure parts; Based on the monitoring stress spectrum data, the statistical counting method and cumulative damage theory are used to diagnose and predict the damage situation of the monitoring points, and the cumulative damage degree and crack initiation life of each monitoring point are given, so as to provide support for enterprises to make maintenance plans, and make the original cumbersome and difficult data acquisition more reliable, safe and convenient, Improve the ability and level of roller coaster monitoring and management.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124528795","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}
More than 50 years aging civil infrastructures have deteriorated, then structural diagnosis and periodic prognosis become critical for predictive maintenance. In terms of the bridge inspection every 5 years in Japan, we have collected a lot of human eye inspection. In context of digital structural monitoring, in addition to the past human inspection we make the most of drone flight images. However, human subjective judge includes individual bias, then a measurable objective score should be quantified using a unified anomaly distance from a health condition. Supervised learning, e.g. classification and semantic segmentation method is not always robust for unseen data. If we address the unlearned blind feature without any experience, prediction error might be a higher hurdle to overcome low precision and less recall problem. The generative anomaly detection via unsupervised learning has been growing in various fields, e.g. medical, manufacturing, food, and materials. If the distance and angle to the target damage interest could be controlled among a feasible range, and if the background noise could be removed and relaxed, then concrete surface damage and steel paint peel or corrosion would enable to discriminate them for predictive maintenance. In this paper, we propose a steel anomaly detector method to compute anomalous scores automatically, where we customize several U-shape skip-connected generator network with patch GAN discriminator. Exactly, we have create an encoder-decoder network using the VGG19 based U-Net generator with a patch discriminator. Furthermore, we explore robust unified anomaly score indicator for the target concrete and painted steel parts to analyze deterioration prognosis, so as to monitor the current status far from a health condition. Finally, focusing on the bridge slab, we exploit toward the inspection images with the number of 10,400, where they contains reinforcement concrete slab at 400 bridges under the direct control of national managers. In order to be stable learning and robust structural health monitoring, we demonstrate to visualize several anomalous feature map for precisely and full-covered digital inspection.
{"title":"BRIDGE SLAB ANOMALY DETECTOR USING U-NET GENERATOR WITH PATCH DISCRIMINATOR FOR ROBUST PROGNOSIS","authors":"Takato Yasuno, Junichiro Fujii, Michihiro Nakajima, Kazuhiro Noda","doi":"10.12783/shm2021/36276","DOIUrl":"https://doi.org/10.12783/shm2021/36276","url":null,"abstract":"More than 50 years aging civil infrastructures have deteriorated, then structural diagnosis and periodic prognosis become critical for predictive maintenance. In terms of the bridge inspection every 5 years in Japan, we have collected a lot of human eye inspection. In context of digital structural monitoring, in addition to the past human inspection we make the most of drone flight images. However, human subjective judge includes individual bias, then a measurable objective score should be quantified using a unified anomaly distance from a health condition. Supervised learning, e.g. classification and semantic segmentation method is not always robust for unseen data. If we address the unlearned blind feature without any experience, prediction error might be a higher hurdle to overcome low precision and less recall problem. The generative anomaly detection via unsupervised learning has been growing in various fields, e.g. medical, manufacturing, food, and materials. If the distance and angle to the target damage interest could be controlled among a feasible range, and if the background noise could be removed and relaxed, then concrete surface damage and steel paint peel or corrosion would enable to discriminate them for predictive maintenance. In this paper, we propose a steel anomaly detector method to compute anomalous scores automatically, where we customize several U-shape skip-connected generator network with patch GAN discriminator. Exactly, we have create an encoder-decoder network using the VGG19 based U-Net generator with a patch discriminator. Furthermore, we explore robust unified anomaly score indicator for the target concrete and painted steel parts to analyze deterioration prognosis, so as to monitor the current status far from a health condition. Finally, focusing on the bridge slab, we exploit toward the inspection images with the number of 10,400, where they contains reinforcement concrete slab at 400 bridges under the direct control of national managers. In order to be stable learning and robust structural health monitoring, we demonstrate to visualize several anomalous feature map for precisely and full-covered digital inspection.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131383963","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}
CHRISTINE-OMEIRA Ibrahim, J. Simon, Lennart T FOX, J. Moll, MARK-FELIX Schütz
The development of unmanned aerial vehicles (UAV) has progressed very rapidly in recent years and finds many applications in various industrial fields. The technological progress brings serious challenges, especially regarding flight safety, as any unexpected behavior of the drone can lead to serious consequences. The autonomous usage of delivery drones requires a high reliability especially in urban areas. This motivates the development and application of structural health monitoring (SHM) systems. In this work, we present and discuss the results of preliminary experiments of a vibration-based SHM system for delivery drones. In particular, we focus on the hover phase during take-off in which the airworthiness can be assessed within seconds. For this purpose, the drone is first launched and a measurement is made using acceleration sensors. The measurement data is evaluated using three different metrics one of which is the Nullspace-Based Fault Detection (NSFD) method. It was demonstrated here that added masses can be detected through the analysis of mechanical vibrations.
{"title":"VIBRATION-BASED STRUCTURAL HEALTH MONITORING OF DELIVERY DRONES: ANALYSIS OF PRELIMINARY EXPERIMENTS","authors":"CHRISTINE-OMEIRA Ibrahim, J. Simon, Lennart T FOX, J. Moll, MARK-FELIX Schütz","doi":"10.12783/shm2021/36242","DOIUrl":"https://doi.org/10.12783/shm2021/36242","url":null,"abstract":"The development of unmanned aerial vehicles (UAV) has progressed very rapidly in recent years and finds many applications in various industrial fields. The technological progress brings serious challenges, especially regarding flight safety, as any unexpected behavior of the drone can lead to serious consequences. The autonomous usage of delivery drones requires a high reliability especially in urban areas. This motivates the development and application of structural health monitoring (SHM) systems. In this work, we present and discuss the results of preliminary experiments of a vibration-based SHM system for delivery drones. In particular, we focus on the hover phase during take-off in which the airworthiness can be assessed within seconds. For this purpose, the drone is first launched and a measurement is made using acceleration sensors. The measurement data is evaluated using three different metrics one of which is the Nullspace-Based Fault Detection (NSFD) method. It was demonstrated here that added masses can be detected through the analysis of mechanical vibrations.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134315463","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}
Recent major accidents related to bridges have emphasized the need for developing effective technological solutions for defect detection, which can minimize the possibility of bridge-related accidents in the future. In this respect, this research will focus towards development of automated system for the detection of defective regions within different steel parts of bridges. At present, there is no open-source image dataset, which can be used for this purpose. Consequently, the training dataset has been developed by using images acquired from bridges in Vietnam and validation was performed using images acquired from Lovelock bridge situated at Highway-80, Lovelock, NV, USA. A total of 5,500 (4,000 images for training and 1,500 for validation) images of different dimensions have been used the original dimensions of the steel bridge images have been modified 572 × 572 pixels, which have been used for the training and evaluation of the dataset on different Deep Encoder-Decoder networks. The use of diverse data from different bridges will allow the development of a robust Deep Encoder-Decoder network with considerable implications for practical systems in the future. This study will employ state-of-the-art Deep Encoder-Decoder network, which have been recently developed for other applications. However, no such study has been developed for defect detection in steel bridges. A comparative evaluation of different Deep Encoder-Decoder networks will be examined. At the same time, the performance of the system will be compared with recent advanced approaches. The results reveal the considerable potential of Deep Encoder-Decoder towards defect detection of steel bridges, which will be further exploited in the future studies.
{"title":"STEEL DEFECT DETECTION IN BRIDGES USING DEEP ENCODER-DECODER NETWORKS","authors":"Habib Ahmed, H. La","doi":"10.12783/shm2021/36335","DOIUrl":"https://doi.org/10.12783/shm2021/36335","url":null,"abstract":"Recent major accidents related to bridges have emphasized the need for developing effective technological solutions for defect detection, which can minimize the possibility of bridge-related accidents in the future. In this respect, this research will focus towards development of automated system for the detection of defective regions within different steel parts of bridges. At present, there is no open-source image dataset, which can be used for this purpose. Consequently, the training dataset has been developed by using images acquired from bridges in Vietnam and validation was performed using images acquired from Lovelock bridge situated at Highway-80, Lovelock, NV, USA. A total of 5,500 (4,000 images for training and 1,500 for validation) images of different dimensions have been used the original dimensions of the steel bridge images have been modified 572 × 572 pixels, which have been used for the training and evaluation of the dataset on different Deep Encoder-Decoder networks. The use of diverse data from different bridges will allow the development of a robust Deep Encoder-Decoder network with considerable implications for practical systems in the future. This study will employ state-of-the-art Deep Encoder-Decoder network, which have been recently developed for other applications. However, no such study has been developed for defect detection in steel bridges. A comparative evaluation of different Deep Encoder-Decoder networks will be examined. At the same time, the performance of the system will be compared with recent advanced approaches. The results reveal the considerable potential of Deep Encoder-Decoder towards defect detection of steel bridges, which will be further exploited in the future studies.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133953305","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}
In this paper, the axioms of Structural Health Monitoring (SHM) presented by Worden et al. [2007] are discussed in detail. In many cases, the intent of the axioms is found to be correct, but the terminologies used are confusing. Also, it was found that some axioms could be derived from other axioms that were given in the paper. Based on the discussion presented in this paper, it is suggested to replace the seven axioms given by Worden et al. [2007] with a set of three new axioms. Counter-examples are presented to dispute the axioms where necessary. Similar to Worden et al. [2007], the term axiom is used outside its meaning in the field of mathematics and logic. In both the papers, the term axiom refers to the fundamental truths, which cannot be contradicted in the field of SHM.
本文详细讨论了Worden等人[2007]提出的结构健康监测(SHM)公理。在许多情况下，发现公理的意图是正确的，但是使用的术语令人困惑。此外，还发现一些公理可以由文中给出的其他公理推导出来。基于本文的讨论，我们建议将Worden et al.[2007]给出的7个公理替换为一组3个新公理。在必要时提出反例来反驳公理。与Worden等人[2007]类似，公理一词在数学和逻辑领域被用于其含义之外。在这两篇论文中，“公理”一词指的是在SHM领域中不能被反驳的基本真理。
{"title":"A NOTE ON AXIOMS OF STRUCTURAL HEALTH MONITORING","authors":"Akash Dixit","doi":"10.12783/shm2021/36340","DOIUrl":"https://doi.org/10.12783/shm2021/36340","url":null,"abstract":"In this paper, the axioms of Structural Health Monitoring (SHM) presented by Worden et al. [2007] are discussed in detail. In many cases, the intent of the axioms is found to be correct, but the terminologies used are confusing. Also, it was found that some axioms could be derived from other axioms that were given in the paper. Based on the discussion presented in this paper, it is suggested to replace the seven axioms given by Worden et al. [2007] with a set of three new axioms. Counter-examples are presented to dispute the axioms where necessary. Similar to Worden et al. [2007], the term axiom is used outside its meaning in the field of mathematics and logic. In both the papers, the term axiom refers to the fundamental truths, which cannot be contradicted in the field of SHM.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127755367","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}
Lamb wave-based damage detection has been demonstrated to be an efficacious method for structural health monitoring (SHM) in general, and corrosion in particular, and is thus deployed in this study. Since a large amount of data is needed for the deep learning networks, this study relies heavily on simulations as the data source and the waveforms are thus generated using simulations. The propagation of the Lamb waves is determined by finite element analysis which is carried out using ABAQUS. The signal features are extracted using continuous wavelet transform for amplitude change observation for presence and extent of the damage. One of the key aspects this paper focuses on is the application of the SHM methodology proposed here for realistic dimensions of corrosion pits. Thus, damage sizes are considered which fall in the range of pitting corrosion morphologies. Simulations are carried out with idealized corrosion pits of varying depths. Methods based on Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) are used for the inverse problem solution to find the damage parameters and are compared with the numerical results. The results show much promise and could be a viable means of detecting corrosion in aircraft structures.
{"title":"ACTIVE DEEP LEARNING-BASED CORROSION DAMAGE DETECTION IN AIRCRAFT STRUCTURES","authors":"Yalew Mekonnen Fenta, G. Kamath","doi":"10.12783/shm2021/36295","DOIUrl":"https://doi.org/10.12783/shm2021/36295","url":null,"abstract":"Lamb wave-based damage detection has been demonstrated to be an efficacious method for structural health monitoring (SHM) in general, and corrosion in particular, and is thus deployed in this study. Since a large amount of data is needed for the deep learning networks, this study relies heavily on simulations as the data source and the waveforms are thus generated using simulations. The propagation of the Lamb waves is determined by finite element analysis which is carried out using ABAQUS. The signal features are extracted using continuous wavelet transform for amplitude change observation for presence and extent of the damage. One of the key aspects this paper focuses on is the application of the SHM methodology proposed here for realistic dimensions of corrosion pits. Thus, damage sizes are considered which fall in the range of pitting corrosion morphologies. Simulations are carried out with idealized corrosion pits of varying depths. Methods based on Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) are used for the inverse problem solution to find the damage parameters and are compared with the numerical results. The results show much promise and could be a viable means of detecting corrosion in aircraft structures.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126762455","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}
Interaction of the monochromatic ultrasonic wave with the uniformly distributed micro-scale damages modeled as a weakly nonlinear solid generates higher harmonics. In practice, most of the metallic components under fatigue and fracture introduce highly localized early-stage damages like local plasticity and region of micro-cracks resulting in shear bands, etc. Theoretical studies by Tang (2012), Wang and Achenbach (2017), Kube (2018), and Wang et.al. (2019) on the interaction of the ultrasonic waves with localized material nonlinearities discuss the interesting effects of the scattered harmonic waves. Low amplitudes of the backscattered harmonic waves and the requirement of a specific set of wideband transducers make experimental studies hard. On the other hand, finite element studies presented in this paper demonstrate the interesting nature of harmonic scattering of the SH waves which will be helpful for the effective design of the laboratory experiments. Generation of backscattered Lamb waves by the interaction of SH wave with local damage due to monochromatic wave is verified with the analytical solution presented by Wang et.al. (2019). Only odd harmonics of the forward scattered SH waves are noted and only even harmonics of both backscattered and forward scattered Lamb waves are noted. Higher amplitudes of static components of Lamb waves are observed due to their cumulative nature similar to higher harmonics. The effect of harmonic scattering in nonlinear guided wave mixing is also studied by considering one-way and two-way two-wave mixing of SH waves. A greater number of sum and difference frequencies along with the odd and even harmonics of both the backscattered and forward scattered waves are noted in codirectional wave mixing, as the complete local damage region is covered by the mixing zone. In two-way mixing, the zero group velocity Lamb waves are generated at the local nonlinear material region. Both backscattered and forward scattered waves contain only Lamb waves with sum and difference frequencies and corresponding odd and even harmonics. To understand the effect of the intensity and size of the localized nonlinear material region various studies are carried out by scaling nonlinear material parameters and geometric size of the local damages (0-20 mm). Observed various characteristics of harmonically scattered waves show their potential in quantifying the intensity, size, and position of local damages by solving simple inverse problems.
单色超声与均匀分布的弱非线性固体微尺度损伤的相互作用产生高次谐波。在实际应用中，大多数金属构件在疲劳断裂过程中都会出现局部塑性、微裂纹区域产生剪切带等高度局部化的早期损伤。Tang(2012)、Wang and Achenbach(2017)、Kube(2018)和Wang等人的理论研究。(2019)关于超声波与局部材料相互作用的研究，非线性讨论了散射谐波的有趣效应。低幅值的后向散射谐波和特定的一组宽带换能器的要求使得实验研究变得困难。另一方面，本文的有限元研究表明了SH波谐波散射的有趣性质，这将有助于有效地设计实验室实验。用Wang等人的解析解验证了SH波与单色波局部损伤相互作用产生后向散射Lamb波。(2019)。前向散射的SH波只有奇次谐波，后向散射和前向散射的Lamb波都只有偶次谐波。由于兰姆波的累积性质类似于高次谐波，因此观察到兰姆波静态分量的振幅较高。通过考虑SH波的单向和双向两波混频，研究了谐波散射对非线性导波混频的影响。在共向混频中，后向散射波和前向散射波的和频和差频以及奇偶谐波都较多，因为整个局部损伤区域被混频区覆盖。双向混合时，在局部非线性材料区产生零群速度兰姆波。后向散射波和前向散射波都只包含和频和差频的兰姆波以及相应的奇偶谐波。为了了解局部非线性材料区域的强度和尺寸的影响，通过缩放非线性材料参数和局部损伤的几何尺寸(0-20 mm)进行了各种研究。观测到的谐波散射波的各种特性显示了它们在通过求解简单逆问题来量化局部损伤的强度、大小和位置方面的潜力。
{"title":"HARMONIC SCATTERING OF SH WAVES FROM A LOCALIZED DAMAGE: FINITE ELEMENT STUDIES","authors":"Pravinkumar R. Ghodake","doi":"10.12783/shm2021/36362","DOIUrl":"https://doi.org/10.12783/shm2021/36362","url":null,"abstract":"Interaction of the monochromatic ultrasonic wave with the uniformly distributed micro-scale damages modeled as a weakly nonlinear solid generates higher harmonics. In practice, most of the metallic components under fatigue and fracture introduce highly localized early-stage damages like local plasticity and region of micro-cracks resulting in shear bands, etc. Theoretical studies by Tang (2012), Wang and Achenbach (2017), Kube (2018), and Wang et.al. (2019) on the interaction of the ultrasonic waves with localized material nonlinearities discuss the interesting effects of the scattered harmonic waves. Low amplitudes of the backscattered harmonic waves and the requirement of a specific set of wideband transducers make experimental studies hard. On the other hand, finite element studies presented in this paper demonstrate the interesting nature of harmonic scattering of the SH waves which will be helpful for the effective design of the laboratory experiments. Generation of backscattered Lamb waves by the interaction of SH wave with local damage due to monochromatic wave is verified with the analytical solution presented by Wang et.al. (2019). Only odd harmonics of the forward scattered SH waves are noted and only even harmonics of both backscattered and forward scattered Lamb waves are noted. Higher amplitudes of static components of Lamb waves are observed due to their cumulative nature similar to higher harmonics. The effect of harmonic scattering in nonlinear guided wave mixing is also studied by considering one-way and two-way two-wave mixing of SH waves. A greater number of sum and difference frequencies along with the odd and even harmonics of both the backscattered and forward scattered waves are noted in codirectional wave mixing, as the complete local damage region is covered by the mixing zone. In two-way mixing, the zero group velocity Lamb waves are generated at the local nonlinear material region. Both backscattered and forward scattered waves contain only Lamb waves with sum and difference frequencies and corresponding odd and even harmonics. To understand the effect of the intensity and size of the localized nonlinear material region various studies are carried out by scaling nonlinear material parameters and geometric size of the local damages (0-20 mm). Observed various characteristics of harmonically scattered waves show their potential in quantifying the intensity, size, and position of local damages by solving simple inverse problems.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116598862","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}
Structural Health Monitoring (SHM) systems promise to improve cost efficiency in aircraft maintenance. Beyond the cost of developing and procuring SHM systems, however, a potentially adverse impact on aircraft performance may negatively affect operational cost. With this in mind, we use an SHM sensor-network model to derive optimal SHM configurations, considering instrumentation and fuel costs as well as saved inspection time, on individual structural component level. Based on Net Present Value theory, we find that retrofitting provides a 20-% benefit on fleet level over factory-only instrumentation, considering the increasing maintenance effort throughout aircraft life as well as variations in individual aircraft usage. We also show that a Value of Information analysis supports more gainful decisions regarding the optimal set of instrumented parts as well as retrofitting times, considering individual aircraft usage.
{"title":"RETROFITTING POTENTIALS IN AIRCRAFT STRUCTURAL HEALTH MONITORING—A VALUE OF INFORMATION ANALYSIS","authors":"Kai-Daniel Büchter, L. Koops","doi":"10.12783/shm2021/36238","DOIUrl":"https://doi.org/10.12783/shm2021/36238","url":null,"abstract":"Structural Health Monitoring (SHM) systems promise to improve cost efficiency in aircraft maintenance. Beyond the cost of developing and procuring SHM systems, however, a potentially adverse impact on aircraft performance may negatively affect operational cost. With this in mind, we use an SHM sensor-network model to derive optimal SHM configurations, considering instrumentation and fuel costs as well as saved inspection time, on individual structural component level. Based on Net Present Value theory, we find that retrofitting provides a 20-% benefit on fleet level over factory-only instrumentation, considering the increasing maintenance effort throughout aircraft life as well as variations in individual aircraft usage. We also show that a Value of Information analysis supports more gainful decisions regarding the optimal set of instrumented parts as well as retrofitting times, considering individual aircraft usage.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"266 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114963376","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}
Xin Peng, Gaofeng Su, ZhiQiang Chen, Raja Sengupta
Visual damage inspection for civil structures is a labor-intensive and timeconsuming task. We propose an autonomous UAV-based pipeline for crack and spalling detection, localization, and quantification. Through fusing 3-dimensional (3D) reconstruction and 2D damage detection after performing UAV-based imaging for an engineering structure, the process generates a damage-annotated 3D information model with rich metadata, including the size and type of damage and its location relative to the structure. The pipeline is composed of four steps: image acquisition via UAV, 3D scene reconstruction, crack/spalling detection and extraction using a deep neural network, and 3D damage localization and quantification. To validate this process, UAV images from three full-scale concrete columns are processed, and results are evaluated in this paper. The results demonstrate that the proposed pipeline can provide accurate and informative 3D condition mapping for civil structures. The authors envision that by employing this UAV-based automatic process, structural damage inspection can be conducted much frequently and rapidly with a significantly low cost.
{"title":"STRUCTURAL DAMAGE DETECTION, LOCALIZATION, AND QUANTIFICATION VIA UAV-BASED 3D IMAGING","authors":"Xin Peng, Gaofeng Su, ZhiQiang Chen, Raja Sengupta","doi":"10.12783/shm2021/36235","DOIUrl":"https://doi.org/10.12783/shm2021/36235","url":null,"abstract":"Visual damage inspection for civil structures is a labor-intensive and timeconsuming task. We propose an autonomous UAV-based pipeline for crack and spalling detection, localization, and quantification. Through fusing 3-dimensional (3D) reconstruction and 2D damage detection after performing UAV-based imaging for an engineering structure, the process generates a damage-annotated 3D information model with rich metadata, including the size and type of damage and its location relative to the structure. The pipeline is composed of four steps: image acquisition via UAV, 3D scene reconstruction, crack/spalling detection and extraction using a deep neural network, and 3D damage localization and quantification. To validate this process, UAV images from three full-scale concrete columns are processed, and results are evaluated in this paper. The results demonstrate that the proposed pipeline can provide accurate and informative 3D condition mapping for civil structures. The authors envision that by employing this UAV-based automatic process, structural damage inspection can be conducted much frequently and rapidly with a significantly low cost.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121892436","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}
Structural health monitoring (SHM) has been an active research area for the last three decades, and has accumulated a number of critical advances over that period, as can be seen in the literature. However, SHM is still facing challenges because of the paucity of damage-state data, operational and environmental fluctuations, repeatability issues, and changes in boundary conditions. These issues present as inconsistencies in the captured features and can have a huge impact on the practical implementation, but more critically on the generalisation of the technology. Population-based SHM has been designed to address some of these concerns by modelling and transferring missing information using data collected from groups of similar structures. In this work, an experimental campaign is discussed, in which vibration data were collected over a series of tests on a set of four healthy, full-scale composite helicopter blades. During the tests, variability was introduced by adjusting boundary conditions between each testing repetition. It is well known that changes of boundary conditions, even from careful repositioning of the structure, can alter selected feature’s properties, changing dynamic responses from normal condition and thus raising false alarms which degrade the effectiveness of SHM. In addition, nominally-identical structures may have slight differences in geometry and/or material properties. These variations can present as changes in the dynamic characteristics of the structure, which can be very problematic for SHM based on machine learning. This paper demonstrates the applicability of SHM when such deviations occur. In this work, a normal condition for the set of helicopter blades is established and tested via a point-wise outlier analysis approach and by defining a general model for the blades, called a population form, using Gaussian process regression.
{"title":"INVESTIGATING EXPERIMENTAL REPEATABILITY AND FEATURE CONSISTENCY IN VIBRATION-BASED SHM","authors":"T. Dardeno, L. Bull, N. Dervilis, K. Worden","doi":"10.12783/shm2021/36346","DOIUrl":"https://doi.org/10.12783/shm2021/36346","url":null,"abstract":"Structural health monitoring (SHM) has been an active research area for the last three decades, and has accumulated a number of critical advances over that period, as can be seen in the literature. However, SHM is still facing challenges because of the paucity of damage-state data, operational and environmental fluctuations, repeatability issues, and changes in boundary conditions. These issues present as inconsistencies in the captured features and can have a huge impact on the practical implementation, but more critically on the generalisation of the technology. Population-based SHM has been designed to address some of these concerns by modelling and transferring missing information using data collected from groups of similar structures. In this work, an experimental campaign is discussed, in which vibration data were collected over a series of tests on a set of four healthy, full-scale composite helicopter blades. During the tests, variability was introduced by adjusting boundary conditions between each testing repetition. It is well known that changes of boundary conditions, even from careful repositioning of the structure, can alter selected feature’s properties, changing dynamic responses from normal condition and thus raising false alarms which degrade the effectiveness of SHM. In addition, nominally-identical structures may have slight differences in geometry and/or material properties. These variations can present as changes in the dynamic characteristics of the structure, which can be very problematic for SHM based on machine learning. This paper demonstrates the applicability of SHM when such deviations occur. In this work, a normal condition for the set of helicopter blades is established and tested via a point-wise outlier analysis approach and by defining a general model for the blades, called a population form, using Gaussian process regression.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"9 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120900493","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：