Structural Health Monitoring最新文献

{"title":"Multicase structural damage classification based on semisupervised generative adversarial network","authors":"Feng-Liang Zhang, Xiao Li, Chul-Woo Kim, He-Qing Mu","doi":"10.1177/14759217241258785","DOIUrl":"https://doi.org/10.1177/14759217241258785","url":null,"abstract":"With the rapid development of computer science and the need for structural safety assessment, structural health monitoring (SHM) systems are widely used in structures. SHM systems primarily rely on sensor systems to collect data related to structural safety conditions, which are then analyzed and assessed for performance evaluation. However, structures in real world are often affected by many uncertain factors, making damage detection based on pattern recognition still difficult to apply. In recent years, research on damage recognition based on machine learning has gained considerable attention. One of the research directions is to use machine learning algorithms to extract features from the dynamic response of structures. Aiming at the problem of inaccurate recognition by machine learning in the case of fewer label samples, this paper proposes a structural state classification method based on semisupervised deep learning. The method is verified on the vibration data of a steel truss bridge and a three-story framework structure to realize the classification of structural states under different working conditions. Unlike the general semisupervised learning method, this paper introduces the mean square error (MS) loss function in the loss function of generative adversarial networks (GANs), thereby enhancing the model training effect (mean square error-generative adversarial networks, MS-GAN). The semisupervised learning uses a small amount of supervised information to guide GAN and then sorts and screens unsupervised data through joint probability, which can reduce labeling costs and improve model accuracy. Compared with the general semisupervised GAN, the algorithm proposed in this paper makes full use of some labeled samples to enable the state recognition and classification of semisupervised learning. By properly utilizing labeled data, the accuracy of state recognition is significantly improved. Finally, a range of training tasks are implemented in order to enhance the classification capability of the proposed MS-GAN through the establishment of varying supervised ratios.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"59 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804715","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 new damage detector for bridges based on natural frequencies with missing data","authors":"A. C. Dederichs, Gabriel A del Pozo, B. T. Svendsen, O. Øiseth","doi":"10.1177/14759217241259621","DOIUrl":"https://doi.org/10.1177/14759217241259621","url":null,"abstract":"Automatic structural health monitoring can simplify the surveillance process of many structures and bridges if its underlying methods return correct interpretations of the structural state. A common method to differentiate between a damaged and undamaged state of a structure is to use its modal properties from an assumed undamaged state to build a baseline to which all new information is compared. The comparison can be performed by calculating the Mahalanobis squared distance (MSD) of natural frequencies. Considering the inherent uncertainties associated with automatic system identification, a new novelty detection algorithm is proposed in this work, intended to work with missing and randomly available natural frequency information, like the outcome of automatic operational modal analysis and mode tracking algorithms. The moments of a multivariate normal distribution used to characterize the bridge’s undamaged behavior are determined elementwise. The damage indicator measures the MSD of new data points to this distribution considering the available natural frequencies and normalizes it using the chi-squared nature of the MSD. The proposed method works as intended for two numerical cases with 25% of the natural frequency values missing at random, where all but the smallest of damages become clearly detectable. It is also tested on two real-world bridges, one of which has a small, controlled change to its structural state. The automatic operational modal analysis of the bridges’ data recordings leads to randomly missing natural frequency values. Despite this, the damage can be detected by the proposed novelty detection algorithm.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"54 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802400","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":"Analysis of the galleries cracking causes in the backfill area of pumped storage power station based on monitoring and numerical simulation: a case study of Hohhot upper reservoir","authors":"Wencong Qiu, Yanlong Li, L. Wen, Zheng Si, Ye Zhang, Yongtao Duan","doi":"10.1177/14759217241259967","DOIUrl":"https://doi.org/10.1177/14759217241259967","url":null,"abstract":"Pumped storage power stations usually arrange galleries in the backfill area at the bottom of the reservoir basin. Under the influence of uneven deformation, the galleries may be difficult to adapt to deformation and generate cracking, which can affect dam safety. In this study, the upper reservoir of Hohhot pumped storage power station was taken as a case study. Through a combination of monitoring data and numerical simulation, the deformation characteristics of the galleries on the backfill foundation were analyzed, and the causes and mechanisms of galleries cracking and structural joints damage were revealed. The in situ monitoring records cover the internal settlement of the dam, the deformation and seepage flow of the galleries, and the ambient temperature. Based on actual engineering data, a numerical model considering the structure and filling method of dam, backfill area, and gallery was established, and the calculation parameters of rockfill material constitutive model were inverted by the direct back analysis method. The monitoring data analysis and numerical calculations showed that the long length of the gallery and the sudden drop of the ambient temperature were the main reasons for the longitudinal microcracks in the top arch of the galleries in the backfill area; the strong constraint of bedrock and the uneven settlement of backfill foundation were the root causes for the penetrating cracks in the galleries at the junction of backfill area and bedrock. In addition, the depth of the gallery embedded in the bedrock determines the deformation form (torsional deformation or bending deformation) of the galleries at the junction of the backfill area and bedrock. Based on the monitoring and numerical simulation, the long-term deformation of the galleries and the development of structural joints were also predicted.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802817","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 multi-order moment matching-based unsupervised domain adaptation with application to cross-working condition fault diagnosis of rolling bearings","authors":"Qi Chang, Congcong Fang, Wei Zhou, Xianghui Meng","doi":"10.1177/14759217241262386","DOIUrl":"https://doi.org/10.1177/14759217241262386","url":null,"abstract":"Unsupervised domain adaptation-based transfer learning (TL) has been widely used in rolling bearing fault diagnosis to overcome the problem of limited and non-identically distributed labeled data. Discrepancy-based alignment is a popular domain adaptation method in TL. However, due to the inability to completely eliminate domain drift, the classifier learned from the source domain may easily misclassify some target domain samples that are scattered near the decision edge. In this work, a multi-order moment matching-based domain adaptation is proposed to address the issue. Low- and high-order moment matching is simultaneously applied to describe the complex non-Gaussian distributions in more detail and realize coarse- and fine-grained hybrid domain alignment. Furthermore, a discriminative clustering approach is employed to extract domain-invariant features of inter-class discrimination and intra-class compactness, which effectively reduces the negative transfer caused by hard-aligned target samples. The application of the proposed model to the experimental dataset demonstrates that the model can significantly improve the diagnosis accuracy of rolling bearing faults in cross-working conditions. This study can be of assistance to engineers in promptly identifying and addressing rolling bearing faults, ultimately enhancing the reliability and safety of equipment.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"25 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804511","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":"Enhancing rubber rupture detection in rubber bearing through generative adversarial network and feature-bagging zero-shot methodology","authors":"Yi Zeng, Chubing Deng, Feng Xiong","doi":"10.1177/14759217241264096","DOIUrl":"https://doi.org/10.1177/14759217241264096","url":null,"abstract":"Base isolation technology is a design strategy developed to protect buildings from the direct impact of seismic forces, utilizing base isolation devices, with rubber bearings being the most commonly used type. After an earthquake, manually inspecting rubber bearings for damage is inefficient, unable to reveal internal damages, and carries significant risks. Consequently, there is a pressing need for an innovative damage detection method. The difficulty of obtaining and labeling data related to rubber rupture damage makes it hard to apply supervised learning methods to construct damage detection models. In response to this, this study combined the active sensing method with unsupervised learning based on feature bagging, establishing a robust rubber damage detection model that successfully addressed the zero-shot problem faced in rubber damage detection processes. To increase the proportion of data on rubber damage, a generative adversarial network based data augmentation methods were applied. The research findings demonstrated that the developed model achieved an average precision of 0.9216 and an area under the ROC curve (Receiver Operating Characteristic curve) of 0.9788 for rupture damage detection, outperforming other machine learning models.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"104 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802450","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 temperature compensation approach using dynamic time warping for electro-mechanical admittance-based concrete structural damage identification","authors":"Hedong Li, Yaozhi Luo, D. Ai","doi":"10.1177/14759217241261919","DOIUrl":"https://doi.org/10.1177/14759217241261919","url":null,"abstract":"Ambient temperature effect on electro-mechanical admittance (EMA) signals always imposes a threat to the accurate identification of concrete damages when using surface-mounted piezoelectric lead zirconate titanate transducers. To reduce adverse temperature effect on the EMA signals, this paper proposed a dynamic-time-warping-based temperature compensation approach for concrete structural damage identification, making use of the similarity between two series via accumulating Euclidean distance under admissible temporal alignments. Validating experiments were conducted on a lab-scaled concrete cube with artificial cracks, and practical application on a full-scaled assembled tunnel structure undergone bolt-loosened defects. The approach was sufficiently verified through comparing with the traditional effective frequency shift method for restoration of the conductance signatures altered by temperature. Experimental results demonstrated that the approach possessed superior performance both for pure temperature compensation and temperature-compensated damage identification to the traditional one, which is promising for practical extension to the in situ concrete infrastructural health monitoring.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803470","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":"Multi-time Lamb waves space wavenumber imaging method based on ultrasonic-guided wavefield","authors":"Zenghua Liu, Xiaoyu Liu, Yanping Zhu, Zhaojing Lu, Long Chen, Bin Wu, Cunfu He","doi":"10.1177/14759217241261091","DOIUrl":"https://doi.org/10.1177/14759217241261091","url":null,"abstract":"Ultrasonic-guided waves full wavefield scanning technology can realize non-destructive testing with non-contact. The damage detection method based on ultrasonic-guided waves’ full wavefield data is widely used in thin-walled plate structures. The defect imaging method based on full wavefield data faces the challenge of simultaneously obtaining the defect contour and thickness direction information. Based on the ultrasonic-guided waves full wavefield scanning detection technology, this paper proposes a multi-time Lamb waves space wavenumber imaging method. In this method, the analytic signal of Lamb wavefield at each moment is constructed by Hilbert transform, and the phase unwrapping is carried out by amplitude sorting and multi-clustering method. Then the wavenumber information is obtained by calculating the phase gradient. The wavenumber at different times of each measurement point is arranged in descending order, and the median wavenumber is extracted as the wavenumber at each measurement point. Based on the dispersion relationship of the tested specimen, the quantitative detection of the plate thickness in the detection area is realized. This method is first applied to the simulation model. Then it is applied to the defect imaging and quantitative detection of aluminum plates with rectangular groove defects and carbon fiber reinforced plastics (CFRP) plates with delamination defects. In simulation and experiment, the local wavenumber imaging method, the frequency domain instantaneous wavenumber imaging method, and the multi-time Lamb waves space wavenumber imaging method are used for defect imaging and quantification. The imaging results are compared and analyzed. The result shows that the multi-time Lamb waves space wavenumber imaging method can restore the morphology of groove defects in aluminum plates and delamination defects in CFRP plates, and accurately estimate the depth of groove defects and the location of delamination defects.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"53 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804856","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":"Assessment of thermal fatigue damage in square tubes using second harmonic generation of longitudinal waves","authors":"Min Gao, C. Ng, Lingkai Meng, Xianwen Hu, Yuqiao Cao, A. Kotousov","doi":"10.1177/14759217241257702","DOIUrl":"https://doi.org/10.1177/14759217241257702","url":null,"abstract":"This paper investigates nonlinear guided wave propagation in square tubes with material nonlinearity for the first time. The dispersion characteristics of guided waves in a square aluminum tube are first analyzed and presented. A pair of longitudinal wave modes is selected as fundamental waves and second harmonics. The selected mode pair is identified to meet the internal resonance condition. Then, a three-dimensional finite element model is developed to simulate the second harmonic generation and propagation in the square tube. When the fundamental waves propagate in the square tube, the second harmonics are generated due to the self-interaction of the fundamental waves and inherent material nonlinearity. The generated second harmonics are manifested to be cumulative with the almost linearly increased nonlinear acoustic parameter versus propagation distance. Subsequently, the experiment is carried out to confirm the simulated results and they are considerably consistent. Finally, the generated second harmonics are employed to evaluate different levels of thermal fatigue damage, which is introduced into the square tube by applying increasing thermal cycles. The nonlinear acoustic parameter increases with the growing thermal cycles, which indicates the induced second harmonics are sensitive to thermal fatigue damage. The findings of this study provide insights into practical applications for the detection and evaluation of early-stage damage in square tubes using second harmonic generation of longitudinal waves.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"41 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805119","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":"Spectral feature informed variational model and its applications to machinery fault diagnosis","authors":"Xingxing Jiang, Xin Wang, Q. Song, Guifu Du, Zhongkui Zhu","doi":"10.1177/14759217241257038","DOIUrl":"https://doi.org/10.1177/14759217241257038","url":null,"abstract":"Variational mode extraction (VME), a novel signal decomposition method based on a frequency-domain filter in essence, has recently become a potential tool in fault diagnosis. However, the original VME algorithm is not provided with full self-adaptation, and its performance in the extraction of fault features is subject to predefining the initial parameters, including initial center frequency (ICF) and balance parameter. To address these issues, a spectral feature informed variational model (SFIVM) algorithm is constructed to overcome the defects of parameters setting and efficiently realize the fault diagnosis without prior knowledge. Specifically, a spectral feature detector inspired by the convergence property of ICF is first developed to reveal the spectral features, including the detected center frequencies and boundary frequencies. Then, a balance parameter estimation formula is designed to adaptively determine the target balance parameter by taking advantage of the above spectral features. Finally, a highly efficient decomposition model is proposed to extract the fault-related mode from the vibration signal, where iterative optimization is unnecessary. The effectiveness of the proposed SFIVM method is verified by one simulated and two experimental cases. Moreover, its superiority and high efficiency are demonstrated by comparing it with some advanced and classical fault diagnosis methods.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"41 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807204","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":"Condition monitoring and warning of a belt drive system based on a logical analysis of data regression-based residual control chart","authors":"R. Khalifa, S. Yacout, Samuel Bassetto, Yasser Shaban","doi":"10.1177/14759217241252046","DOIUrl":"https://doi.org/10.1177/14759217241252046","url":null,"abstract":"The belt drive system is commonly used to transmit power in different industrial systems to maintain high performance and safety. Online condition monitoring techniques (CMTs) are used to monitor the operational conditions of such systems. Vibration-based monitoring techniques (VMT) are among the CMTs that are used in the analysis and diagnosis of the state of a belt drive system. Machine learning techniques are integrated with the VMT based on Industry 4.0 aspects for vibration analysis and fault diagnosis. Most of these techniques are based on the collection of vibration data from the belt drive system under known normal and different known faulty operations. This enables a fault to be diagnosed when it is detected during the operation of a system. In this paper, a new condition monitoring and warning mechanism is proposed to monitor the operational conditions of a belt drive system. The mechanism is based on an integration of a logical analysis of data regression (LADR) with a residual control chart (RCC). It uses vibration data from the belt drive system under normal operation only. This mechanism exhibits better performance in fault detection and also in interpreting the root cause of the faults in a belt drive system. Experimental investigations on a belt drive test rig have been carried out to collect vibration data based on a design of experiment for operational factors during normal operation. The LADR-RCC is implemented to monitor the operation of the belt drive system and detect faulty states. The accuracy of LADR is compared with multiple linear regression-based RCC, support vector regression-based RCC and random forest-based RCC. The LADR-RCC demonstrates significant enhancements in fault detection. The advantage of LADR-RCC over other model-based RCC is that it finds the root cause of a fault that is experienced in the system.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"61 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806836","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}