Structural Health Monitoring最新文献

{"title":"Advanced deep learning framework for underwater object detection with multibeam forward-looking sonar","authors":"Liangfu Ge, Premjeet Singh, Ayan Sadhu","doi":"10.1177/14759217241235637","DOIUrl":"https://doi.org/10.1177/14759217241235637","url":null,"abstract":"Underwater object detection (UOD) is an essential activity in maintaining and monitoring underwater infrastructure, playing an important role in their efficient and low-risk asset management. In underwater environments, sonar, recognized for overcoming the limitations of optical imaging in low-light and turbid conditions, has increasingly gained popularity for UOD. However, due to the low resolution and limited foreground-background contrast in sonar images, existing sonar-based object detection algorithms still face challenges regarding precision and transferability. To solve these challenges, this article proposes an advanced deep learning framework for UOD that uses the data from multibeam forward-looking sonar. The framework is adapted from the network architecture of YOLOv7, one of the state-of-the-art vision-based object detection algorithms, by incorporating unique optimizations in three key aspects: data preprocessing, feature fusion, and loss functions. These improvements are extensively tested on a dedicated public dataset, showing superior object classification performance compared to the selected existing sonar-based methods. Through experiments conducted on an underwater remotely operated vehicle, the proposed framework validates significant enhancements in target classification, localization, and transfer learning capabilities. Since the engineering structures have similar geometric shapes to the objects tested in this study, the proposed framework presents potential applicability to underwater structural inspection and monitoring, and autonomous asset management.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140385826","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":"Multiscale fluctuation-based symbolic dynamic entropy: a novel entropy method for fault diagnosis of rotating machinery","authors":"Ao Shen, Yongbo Li, Khandaker Noman, Dong Wang, Zhike Peng, Ke Feng","doi":"10.1177/14759217241237717","DOIUrl":"https://doi.org/10.1177/14759217241237717","url":null,"abstract":"Health monitoring has garnered significant and increasing attention from the research community and industrial practices thanks to its critical role in ensuring the safe operation of machinery and maintenance schedule. With regard to this, this paper introduces a novel diagnostic approach called fluctuation-based symbolic dynamic entropy (FSDE), which can enhance noise immunity and computational efficiency by utilizing fluctuation-based entropy algorithm and symbolic dynamic filtering. Moreover, the noise immunity and computational efficiency of FSDE, permutation entropy, fuzzy entropy, and sample entropy are compared by simulation signals. The simulation results show that FSDE has a more robust anti-noise performance and higher computational efficiency than the other three entropy methods. In order to extract fault features more thoroughly, multiscale analysis is applied to the entropy method, and multiscale FSDE (MFSDE) is proposed. MFSDE divides the measurement data into several scale series by coarse-grained technology, and then, FSDE is used to process each scale series separately. A series of experiments verify the effectiveness of MFSDE in fault feature extraction of fault diagnosis. Furthermore, the experimental results substantiate that MFSDE outperforms the other three currently used entropy-based approaches in terms of accuracy in classifying different health conditions of transmission systems.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140385655","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 method for monitoring the uneven settlement of shield tunnels considering the flattening effect using distributed strain data measured from BOTDA sensors","authors":"Zheng Zhou, Xinteng Ma, Yang Liu, Hu Li","doi":"10.1177/14759217241236368","DOIUrl":"https://doi.org/10.1177/14759217241236368","url":null,"abstract":"When investigating the uneven settlement monitoring of shield tunnels, the influence of the flattening effect under longitudinal bending is rarely considered, which leads to inaccurate and incomplete settlement monitoring. To address this issue, a method for monitoring the uneven settlement of shield tunnels that considers the flattening effect is proposed, which is achieved using high-density strain data from distributed Brillouin optical time domain analysis (BOTDA) sensors. The high-density measured strain data is used to combine global settlement with the additional influence of the flattening effect, which includes extrusion and shear effects. This approach allows the trend of uneven settlement to be effectively monitored. Furthermore, a finite element model updating process is implemented in advance to improve the accuracy of soil parameters and minimize calculation errors in practical engineering. To demonstrate the effectiveness of the proposed method, an application example from the Jinan Rail Transit Line 3 was utilized for numerical simulations and practical tests. The results of the application example reveal that the proposed method has a high accuracy, with measurements comparable to the total station-based method. This indicates that the proposed method is an effective and robust settlement monitoring approach and provides a referential basis for further tunnel operations and maintenance.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140210411","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":"Deep neural network for damage detection in Infante Dom Henrique bridge using multi-sensor data","authors":"Ana Fernandez-Navamuel, D. Pardo, Filipe Magalhães, Diego Zamora-Sánchez, Á. J. Omella, D. García-Sánchez","doi":"10.1177/14759217241227455","DOIUrl":"https://doi.org/10.1177/14759217241227455","url":null,"abstract":"This paper proposes a data-driven approach to detect damage using monitoring data from the Infante Dom Henrique bridge in Porto. The main contribution of this work lies in exploiting the combination of raw measurements from local (inclinations and stresses) and global (eigenfrequencies) variables in a full-scale structural health monitoring application. We exhaustively analyze and compare the advantages and drawbacks of employing each variable type and explore the potential of combining them. An autoencoder-based deep neural network is employed to properly reconstruct measurements under healthy conditions of the structure, which are influenced by environmental and operational variability. The damage-sensitive feature for outlier detection is the reconstruction error that measures the discrepancy between current and estimated measurements. Three autoencoder architectures are designed according to the input: local variables, global variables, and their combination. To test the performance of the methodology in detecting the presence of damage, we employ a finite element model to calculate the relative change in the structural response induced by damage at four locations. These relative variations between the healthy and damaged responses are employed to affect the experimental testing data, thus producing realistic time-domain damaged measurements. We analyze the receiver operating characteristic curves and investigate the latent feature representation of the data provided by the autoencoder in the presence of damage. Results reveal the existence of synergies between the different variable types, producing almost perfect classifiers throughout the performed tests when combining the two available data sources. When damage occurs far from the instrumented sections, the area under the curve in the combined approach increases [Formula: see text] compared to using local variables only. The classificatoin metrics also demonstrate the enhancement of combining both sources of data in the damage detection task, reaching close to [Formula: see text] precision values for the four considered test damage scenarios. Finally, we also investigate the capability of local variables to localize the damage, demonstrating the potential of including these variables in the damage detection task.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":" 33","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140211490","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":"Post-tensioning tendon force estimation of in-service prestressed concrete structure using cylindrical lamb waves","authors":"Ohjun Kwon, Hoon Sohn, Hyung Jin Lim","doi":"10.1177/14759217241233930","DOIUrl":"https://doi.org/10.1177/14759217241233930","url":null,"abstract":"This paper proposes an ultrasonic-based force estimation technique for the post-tensioning (PT) tendon of an in-service prestressed concrete structure. First, three macro fiber composite transducers are installed on the surface of an in-service PT tendon subjected to an unknown tensile force for the generation and measurement of cylindrical Lamb waves. Then, the velocities of the longitudinal and shear waves are calculated using the measured cylindrical Lamb waves at two ultrasonic input frequencies. Subsequently, the Poisson’s ratio, elastic modulus, and strain induced by the PT tendon force are estimated from the longitudinal and shear wave velocities. Finally, the PT tendon force is obtained from the estimated mechanical property values. The experimental validation was performed using a mono-strand PT tendon under various temperature conditions, and the developed technique estimated the unknown PT tendon force in the range of 10–100 kN with a 6.22 kN maximum error and a 2.99 kN root-mean-square error. The uniqueness of this paper includes (1) no requirement for temperature compensation for field deployment, (2) PT tendon force estimation without calibration of the PT tendon force and cylindrical Lamb wave relationship, and (3) easy sensor installation and no interference with the PT construction process.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":" 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140211419","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":"Vision-based detection of bolt tension considering non-rotatory loosening via a new calculation method of bolt flexibility coefficient","authors":"Yong Zhao, Qingyuan Lin, Yuming Liu, Wei Pan","doi":"10.1177/14759217241233477","DOIUrl":"https://doi.org/10.1177/14759217241233477","url":null,"abstract":"Bolted joint is widely used in construction, vehicle, aerospace, and other engineering fields. Bolt tension is the most important performance index of bolted joints. The whole life cycle monitoring of bolt tension without contact and damage can be realized by vision-based method. The existing methods indirectly predict the change of bolt tension by calculating the rotary angle of the nut. This kind of method can judge the loosening caused by nut rotation but cannot deal with the non-rotary loosening. At the same time, the relationship between nut rotation angle and loosening must be obtained through calibration experiments. This paper proposes a vision-based method to measure the change of bolt tension considering the non-rotary loosening by detecting the change of bolt elongation. Based on VDI2230 and finite element analysis, a new calculation method of bolt flexibility coefficient is proposed. The experimental results show that this method can measure the change of bolt tension with high precision in the process of tightening and loosening.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":" 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140217725","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":"Detecting wire breaks in prestressed concrete pipes: an easy-to-install distributed fibre acoustic sensing approach","authors":"Lisbel Rueda-García, Daniel Tasquer-Val, Pedro Calderón-Bofías, Pedro A Calderón","doi":"10.1177/14759217241236365","DOIUrl":"https://doi.org/10.1177/14759217241236365","url":null,"abstract":"The escalating water stress resulting from drought conditions in certain global regions underscores the imperative to minimize water losses, particularly within drinking water supply networks. One way to achieve this is by improving pipe monitoring systems to allow the early detection of possible structural collapse of the pipes. One type of pipe widely used in water mains is the prestressed concrete pipe, whose main cause of structural failure is the breakage of prestressing wires. This research paper analyses the ability of an easy-to-install distributed acoustic sensing (DAS) monitoring system using fibre optics to identify and locate the acoustic signal produced by the wire breaks in prestressed concrete pipes to make early detection of possible structural failures. For this purpose, a large experimental pipeline stretch was built (approximately 1 m in diameter and 40 m long) where wire breaks were simulated. Several variables were studied: the origin of the signal (to distinguish wire breaks from events of a similar nature), the location of the event in the pipe, the presence of background noise, the internal water pressure, the length of the prestressed wire not subject to bonding with the concrete and the presence of water in the pipe. The results showed that the DAS system could detect almost all events. In addition, two of the multiple parameters measured in the signals, the zero-crossing rate and the short-time energy, made it possible to precisely determine the signal’s origin and the event’s location. Another parameter measured, the duration of the signal in this case, made it possible to differentiate whether the events had occurred when the pipe was empty or full of water. These and other results in this paper present a highly promising perspective on using this DAS system in water main monitoring.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"31 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140226516","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":"Multiscale permutation entropy based on natural visibility graph and its application to rolling bearing fault diagnosis","authors":"Ping Ma, Weilong Liang, Hongli Zhang, Cong Wang, Xinkai Li","doi":"10.1177/14759217241229999","DOIUrl":"https://doi.org/10.1177/14759217241229999","url":null,"abstract":"Rolling bearings being important components of mechanical equipment, the accurate fault diagnosis method of rolling bearings is of great importance to ensure production safety. Permutation entropy is a nonlinear measure of the irregularity of time series, which involves calculating permutation patterns, that is, defining permutations by comparing adjacent values of the time series. When using graph signal processing technology to analyze the vibration signal of rolling bearing, the natural visibility graph (NVG) can better reflect the dynamic characteristics of the vibration signal than path graph (PG). In this paper, the multiscale permutation entropy (MPE) is defined on NVG, and it is used to characterize the different fault characteristics of rolling bearings. The sand cat swarm optimization (SCSO) algorithm is employed to optimize the parameters of support vector machine (SVM); The MPEs of different faults of rolling bearing which defined on NVG are regarded as the fault feature set input into optimized SVM, and it is applied to characterize the different fault characteristics of rolling bearings, realizing fault diagnosis of rolling bearing. The proposed method is used to analyze the experimental data which contain both normal and faulty rolling bearings. The experiment results show that the proposed method can diagnose the bearing faults effectively. The MPE based on NVG is superior to MPE based on PG and MPE based on the vibration signal in distinguishing the different damage states of rolling bearings. The classification accuracy of optimized SVM based on SCSO algorithm is higher than other classical models. The effectiveness and feasibility of defining entropy on the graph signal and as the fault feature vectors for rolling bearing to realize fault diagnosis is validated. The results indicate that the proposed method can effectively detect bearing faults, and demonstrate its effectiveness and robustness for rolling bearing fault diagnosis.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"54 s54","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140230120","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":"Structural rotor rub-impact diagnosis under intricate noise interferences based on targeted component extraction and stochastic resonance enhancement","authors":"Yaochun Hou, Huan Wang, Yuxuan Wang, Peng Wu, W. Huang, Dazhuan Wu","doi":"10.1177/14759217241231897","DOIUrl":"https://doi.org/10.1177/14759217241231897","url":null,"abstract":"Rub-impact is a common nonlinear fault of the rotor system, occurring in rotating machines with radial clearance between the rotor and the stator, which may lead to serious consequences. Since the vibration response of rotor rub-impact is shown as multicomponent with time-varying characteristics of undulatory instantaneous frequency, it is desired to exploit advanced signal processing methods for rub-related feature excavation and failure diagnosis under complex noise interferences, which is of crucial significance to ensure the stable and efficient operation of the whole unit. This paper concerns the processing of acceleration signals and proposes a novel intrawave frequency modulation detection approach for structural rotor rubbing diagnosis based upon targeted component extraction and stochastic resonance enhancement. First, the acquired vibratory acceleration signal is converted into displacement signal via a two-stage integration strategy. Next, to extract the rotating frequency component of high information clarity for further time–frequency analysis from the multicomponent signal, an especially designed improved variational mode decomposition method based on the modified target frequency index is put forward, and the instantaneous frequency of the objective component is estimated. Then, the optimum stochastic resonance is leveraged for intrawave frequency modulation enhancement. Finally, the rotor rub-related symptom can be distinctly revealed and the diagnostic procedure can be performed. The effectiveness and superiority of the proposed rotor rub-impact diagnosis approach are demonstrated through both simulations and experiments, indicating that it is suitable to be implemented in practical applications, with high noise-resistance ability, and can efficiently extract the potential characteristics of rotor rub-impact malfunction from multicomponent signals.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"27 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140235173","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":"Non-contact detection of the interfacial microdefects in metal/CFRP hybrid composites using air-coupled laser ultrasound","authors":"Baoding Wang, Zhongwen Cheng, Weisheng Liao, Bainian Long, Jun-Wei Wu, Lvming Zeng, Xuanrong Ji","doi":"10.1177/14759217241234557","DOIUrl":"https://doi.org/10.1177/14759217241234557","url":null,"abstract":"Metal/CFRP (carbon fiber reinforced plastic) hybrid composites are crucial in aerospace applications, demanding non-contact, high-resolution inspection. Traditional non-destructive testing methods face daunting technical challenges due to varying densities and impedance between fiber and metal layers. Here, a hybrid air-coupled laser-ultrasound (ACLU) system was presented for non-contact detection of internal microdefects on the non-homogeneous metal/CFRP interface. The near-surface resolution of the system can reach as high as 80 µm on a long effective working distance of 50 mm. Three types of experiments, specifically ACLU, phased-array, and X-ray inspection, were performed in Al/CFRP composites with the interfacial microdefects located in Al, bonded, and CFRP layers respectively. Comparing with traditional methods, the proposed ACLU system can detect the microdefects of bonding failure, cohesive failure, and delamination at the Al/CFRP interface with higher contrast, resolution, and sensitivity. A significant advantage of the ACLU system is its ability to detect defects on curved surface structure materials, making it highly versatile for various composite configurations. Our eventual goal is to offer an affordable non-contact laser-ultrasound approach for quality and damage inspection of metal/CFRP hybrid composites.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"54 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140237202","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}