Structural Health Monitoring最新文献

{"title":"Detecting ice on plate-like structures via flow-induced random vibration: a dispersion curve shift identification approach","authors":"Qihang Qin, Xun Wang","doi":"10.1177/14759217241249402","DOIUrl":"https://doi.org/10.1177/14759217241249402","url":null,"abstract":"This paper proposes an ice detection framework for thin plate structures using flow-induced random guided waves measured by two passive sensors. Its principle is based on the fact that the ice accretion shifts the dispersion curve of guided wave, which can be extracted from the cross-correlation of ambient wave fields at two receivers. More specifically, the group velocity under various frequencies is identified from the prominent peak of the cross-correlation with a band-pass filter, which signifies the travel time of a narrow-band guided wave, and this forms a reconstruction of the dispersion curve. The ice thickness is then estimated by minimizing the weighted error between the reconstructed dispersion curve and its theoretical model solved from the Rayleigh–Lamb equations for the two-layer (ice-plate) structure. The weights are assigned to various frequencies according to the global sensitivity of the group velocity versus the ice accretion. The proposed method is assessed by a laboratory experiment where the ice accretion on an aluminum plate is carried out in an air-cooling environment and the random vibration is excited by spraying air jet onto the plate surface. Experimental results show that the ice thickness can be accurately estimated if it is of the same order or even lower than the plate thickness. The proposed method has the potential to realize the long-range real-time detection of aircraft icing conditions where the passive sensors can be tiny, light, free of energy supply, and mounted on the internal surface of fuselage skin without any effect on aircraft aerodynamics.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"141 39","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141350771","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}

{"title":"Damage diagnosis of plates and shells through modal parameters reconstruction using inverse finite-element method","authors":"M. Y. Belur, A. Kefal, M. A. Abdollahzadeh, S. Fassois","doi":"10.1177/14759217241249678","DOIUrl":"https://doi.org/10.1177/14759217241249678","url":null,"abstract":"In this study, a new modal-based structural health monitoring (SHM) approach is proposed based on the inverse finite-element method (iFEM) to perform damage diagnosis of the plate and shell structures based on full-field modal parameters reconstructed from discrete sensor data. The iFEM formulation can effectively solve a shape sensing or deformation reconstruction problem, where changing displacements of the structure are predicted by minimizing a variational least squares error function of analytical and experimental discrete strains with respect to unknown displacements. Such a solution provides the time-domain response of the structures, which may be solely not enough to extract the dynamical properties of the structure for underlying the unhealthy conditions. To address this important gap, the iFEM is enhanced by processing the full-field displacement solution with fast Fourier transformation, enabling mechanical parameters to switch from time to frequency domain. This posterior step, named iFEM Modal Reconstruction (iFEM-MoRe), can recover full-field dynamical characteristics from the response discrete Fourier transformation of a structure for the investigation of unhealthy structural conditions and damage identification. In this regard, iFEM-MoRe allows the utilization of the entire time/frequency-domain response of structures for correlating modal/dynamical characteristics with structural anomalies. To verify the capability of the approach, intact and damaged cases of benchmark problems are solved. According to the results, it is demonstrated that iFEM-MoRe can predict highly precise natural frequencies just from discrete sensor data without loading/material information. Also, it is revealed that iFEM-MoRe can highly accurately reconstruct full-field mode shapes and diagnose damaged conditions by pinpointing alternated dynamical characteristics of structures as compared to intact parameters. Overall, the presented approach can serve as a complementary toolbox for vibration and/or statistical time series SHM methods to understand full-field modal characteristics of damaged cases just from a network of sensors.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141373112","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}

{"title":"Experimental validation of automated OMA and mode tracking for structural health monitoring of transmission towers","authors":"Yacine Bel-Hadj, M. Weil, W. Weijtjens, C. Devriendt","doi":"10.1177/14759217241249048","DOIUrl":"https://doi.org/10.1177/14759217241249048","url":null,"abstract":"This article presents a cost-effective method to monitor the structural health of transmission towers, a critical yet aging infrastructure that plays an important role in the overall reliability of the electrical grid. The method is validated experimentally on a real-world transmission tower which was subjected to several (exaggerated) damage scenarios. The proposed monitoring strategy relies on four accelerometers installed on the four faces of the rectangular base of the transmission tower. The collected vibration data is processed using a classic operational modal analysis (OMA)-based structural health monitoring scheme, comprising; automated OMA, tracking, data normalization, and decision-making. The proposed algorithm processes the four faces independently to maximize the likelihood of detecting (local) damage near the sensors in the quasi-symmetric structure. Furthermore, with widespread deployment in mind, the current article introduces a semi-automated tracking algorithm using “Density-based spatial clustering of applications with noise.” Environmental effects were removed using principal component analysis, eliminating the need for additional (environmental) sensors. Finally, Q and T2 statistics were used to assess damage on each face of the structure using all tracked modes. The experimental results of this study demonstrate that this workflow can effectively track a large number of modes; in the current study, 10 modes per face of the structure, and from them detect and to some level localize the majority of structural damage-inducing events, such as the removal of a bolt or a bar.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"30 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141272852","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}

{"title":"A novel fiber Bragg grating-based smart clamp with macro strain measurement: design, modeling, and application to incipient looseness detection","authors":"Li Zhang, Ze-Chao Wang, Qin Wei, Rui-Ya Li, Zu-De Zhou, Wang-Ji Yan, Yong-Zhi Qu, Liu Hong","doi":"10.1177/14759217241245303","DOIUrl":"https://doi.org/10.1177/14759217241245303","url":null,"abstract":"Ensuring the safety and optimal performance of hydraulic pipelines, particularly in aircraft, is of paramount importance. The challenge of detecting incipient looseness in fasteners within a time-varying temperature environment has garnered widespread recognition, making it a focal point in the field of structural health monitoring. In this study, we propose an innovative solution—a smart clamp based on Fiber Bragg Grating (FBG). This clamp aims to detect incipient looseness in hydraulic pipe systems by measuring its macro strain. Notable advantages include its ability to avoid the chirping of the FBG, the ease of building a distributed sensing network, especially for the blind-hole connected structure, and its applicability for monitoring clamps connected with small-diameter bolt. The analytical model of the clamp under the preload of the connected bolt, along with the unique condition for the measurement, is provided. Demonstrating a force resolution [Formula: see text], the clamp exhibits a strong capability in detecting incipient looseness. Furthermore, environmental temperature interference is mitigated by configuring two FBGs. Conducting application experiments in a hydraulic system, the results indicate that the proposed clamp can effectively detect the loosening and tightening processes in real time, even under the time-varying temperature environment.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"2 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101340","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}

{"title":"Identification of bridge influence line and multiple-vehicle loads based on physics-informed neural networks","authors":"Xingtian Li, Jinsong Zhu","doi":"10.1177/14759217241248570","DOIUrl":"https://doi.org/10.1177/14759217241248570","url":null,"abstract":"Influence lines (ILs) and vehicle loads identification are critical in the design, health monitoring, and damage detection of bridges. Traditionally, the approach used in most existing literature has been to solve the system of equations directly. However, these approaches require complex calculations such as matrix decomposition and regularization coefficient optimization, making them difficult to implement. In addition, there are difficulties in obtaining accurate axle information and effectively separating the bridge response due to each vehicle. Thus, the improvement of identification algorithms for ILs and multi-vehicle loads remains of significant importance. To address these issues, this paper presents a novel approach that integrates prior physical equations and neural networks. This is achieved by integrating the equation that reflects the relationship between axle loads and bridge response into the neural network, utilizing existing methods for acquiring axle information of vehicles. To validate the effectiveness of the proposed method, it was first applied to theoretical and simulation data. The study then investigated the impact of noise and dynamic effects on the accuracy of the results, as well as the range of the neural network layers and sampling intervals. Finally, the method was implemented for identifying multiple-vehicle loads. The findings of the study confirm the feasibility and numerical stability of the proposed approach. The proposed method eliminates the need for complex computational processes, including matrix decomposition, diagonalization, regularization coefficient optimization, and solution vector smoothing fitting. As a result, the implementation of the algorithm is significantly less challenging, and identification accuracy is improved. It is important to note, however, that the proposed method is relatively more time-consuming due to the iterative learning and training required by the neural network.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"11 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141098634","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}

{"title":"Tacho-less spur gear condition monitoring at variable speed operation using the adaptive application of variational mode extraction","authors":"Shahis Hashim, Piyush Shakya","doi":"10.1177/14759217241247867","DOIUrl":"https://doi.org/10.1177/14759217241247867","url":null,"abstract":"Effective condition monitoring of machine components contributes to a safer working environment for operators and assists in averting critical machinery shutdowns. In real-world industrial scenarios, fault detection must remain simplified, user-friendly and robust despite speed variations. The operational demands of machinery frequently result in speed fluctuations, leading to spectral smearing, thereby compromising the efficiency of the analysis methodology. Furthermore, the intricacies of parameter initialisation often increase the complexity of the analysis methods. This study introduces a fault diagnosis algorithm that requires minimal initialisation and operates independently of tacho pulses. The algorithm proposed in the study incorporates variational mode extraction with the maxima tracking algorithm for instantaneous frequency estimation. Hankel matrix-based selective spectral fusion is proposed to mitigate the impact of frequency tracking errors caused by transient noise. The results of spectral side-band-based fault severity analysis, conducted on an in-house spur gearbox test-bed with seeded tooth chips, underscore the superior performance of the proposed algorithm when compared to contemporary non-stationary analysis methods.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"40 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141103575","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}

{"title":"Interpretable domain adaptation transformer: a transfer learning method for fault diagnosis of rotating machinery","authors":"Dongdong Liu, Lingli Cui, Gang Wang, Weidong Cheng","doi":"10.1177/14759217241249656","DOIUrl":"https://doi.org/10.1177/14759217241249656","url":null,"abstract":"Domain adaptation-based transfer learning methods have been widely investigated in fault diagnosis of rotating machinery, but their basic convolution or recurrent structure is subject to poor global feature representation ability, which hinders the learning of domain-irrelevant modulation information. In addition, the “black box” nature of deep learning models limits their applications in high risk-sensitive scenarios. In this paper, an interpretable domain adaptation transformer (IDAT) is proposed for the transferable fault diagnosis of rotating machinery. First, a multi-layer domain adaptation transformer framework is proposed, which can capture the global information that is crucial for learning the modulation information of different domains, and meanwhile reduce the feature distribution discrepancy. Second, an ensemble attention weight is applied to enable the transfer learning framework to be interpretable, which is implemented by averaging the integral values of the multi-head attention maps along the key direction. In addition, the raw vibration signals are embedded as the input of the model, which provides an end-to-end fault diagnosis. The proposed IDAT is tested by various cross-condition and cross-machine bearing fault diagnosis tasks, and results confirm the advantages of the method.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"99 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141106011","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}

{"title":"Comprehensive assessment and monitoring of bond deterioration in GFRP-reinforced concrete beams using guided wave and acoustic emission techniques","authors":"A. I. Rather, Sauvik Banerjee, A. Laskar","doi":"10.1177/14759217241252084","DOIUrl":"https://doi.org/10.1177/14759217241252084","url":null,"abstract":"The debonding between glass fiber-reinforced polymer (GFRP) rebars and concrete significantly reduces the bond strength and jeopardizes the safety and durability of GFRP-reinforced concrete (GFRP-RC) structures. As a result, it is imperative to monitor the structural integrity of infrastructures being built using these composite materials in structural elements during their service lives. Previous studies have primarily focused on artificially induced debonding scenarios for guided wave (GW)-based monitoring, which has resulted in an inability to capture debonding events that arise naturally due to external loads. Furthermore, flexural bond test investigations simulating real loading scenarios in GFRP-RC structures have not been linked with acoustic emission (AE) and GW monitoring. The present study addresses these research gaps by comprehensively examining the flexural bond integrity in GFRP-RC structural elements subjected to realistic loading conditions using a hybrid approach combining GW and AE as active and passive structural health monitoring techniques, respectively. A series of bond tests, conforming to RILEM specifications, have been performed to investigate various parameters (including rebar surface characteristics, embedment length, and confinement conditions) that affect the flexural bond degradation response of GFRP-RC members. Damage indices based on GW and AE signal parameters have been developed to evaluate the characteristics of confined and unconfined specimens distinctly. It has been observed from the test results that the L (0,3) GW mode is more sensitive to debonding than other modes and that a threshold of 25% has been established for the GW damage index to denote significant bond damage. Furthermore, AE damage indices have provided valuable insights into concrete damage, including splitting and damage at the rebar-concrete interface. Thus, a thorough understanding of bond deterioration in GFRP-RC structures has been developed by combining GW and AE health monitoring techniques, with implications for enhancing structural safety and durability.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"34 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141117759","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}

{"title":"Small-sample damage detection of bleacher structure based on GAN and MSS-CNN models","authors":"Chaozhi Cai, Xiaoyu Guo, Jianhua Ren, Yingfang Xue","doi":"10.1177/14759217241252756","DOIUrl":"https://doi.org/10.1177/14759217241252756","url":null,"abstract":"The damage scales and forms of bleacher structure are diverse, and the training by using neural network models may be inadequate when the data sample is limited, resulting in challenges such as overfitting or the inability to generalize new damage scenarios. In order to address the issue of damage detection in bleacher structures with small samples, this paper proposes a multi-scale stride convolutional neural network (MSS-CNN) model. It is trained as a generator and discriminator within a generative adversarial network (GAN) framework. By utilizing GAN to generate data and integrating real data with generated data, the mixed data is input into the MSS-CNN model for training, ultimately yielding damage detection results. In order to validate the effectiveness of this approach, a series of experimental studies are conducted by using a bleacher simulator at Qatar University as the research subject. Furthermore, the model is compared with ResNet, multi-layer perceptron, and support vector machine under identical experimental conditions by comparing real and mixed data. The experimental results consistently demonstrate the superior performance of the MSS-CNN model across multiple experiments. This paper presents a fresh research approach and perspective for addressing the challenge of small-sample damage detection in bleacher structures.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"97 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141116348","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}

{"title":"UAV vision-based crack quantification and visualization of bridges: system design and engineering application","authors":"Liming Zhou, Yuqiu Jiang, Haowen Jia, Liping Zhang, Fei Xu, Yongding Tian, Zhecheng Ma, Xinyu Liu, Shuanglin Guo, Yunpeng Wu, Zhirong Zhao, Hemin Zheng","doi":"10.1177/14759217241251778","DOIUrl":"https://doi.org/10.1177/14759217241251778","url":null,"abstract":"Accurately measuring visible cracks in bridges is crucial for their structural health diagnosis, damage detection, performance evaluation, and maintenance planning. The primary means of visual crack detection still relies heavily on manual visual inspection, an inefficient process that can pose significant safety risks. This article develops a unmanned aerial vehicle (UAV) vision-based surface crack measurement methodology and visualization scheme for the bridges that can detect and measure cracks automatically with improved efficiency. The surface crack measurement methodology is achieved by designing a three-stage crack sensing system including the You Only Look Once-based crack recognition, U-shaped network-based crack segmentation, and deep-vision-based crack width calculation. This workflow is integrated into a comprehensive UAV inspection system, which is intended for operation at the field. The surface crack visualization scheme is accomplished by taking advantage of time-series image fusion, GPS information migration, and three-dimensional (3D) point cloud technique to reconstruct the 3D geometrical model of the tested bridge, which is convenient for unveiling the crack information in the bridge. The proposed methodology was successfully validated by a case study on an arch bridge. The achievement of this article promotes the UAV vision-based bridge’s surface crack inspection technology to a new status that no preparation for pasting calibration marker is needed, and crack identification, segmentation, and width calculation are realized promptly during the UAV flying on-site, as well as damage evaluation for bridges is visually fulfilled based on the reconstructed digital-graphical 3D model. The working environments and influencing factors to the developed system are sufficiently discussed. Certain limitations in the current application are pointed out for future improvements.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"32 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119044","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}