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Automated instance segmentation of asphalt pavement patches based on deep learning Struct. Health Monit. (IF 6.6) Pub Date : 2024-04-17 Anzheng He, Allen A Zhang, Xinyi Xu, Yue Ding, Hang Zhang, Zishuo Dong
The location and pixel-level information of the patch are all critical data for the quantitative evaluation of pavement conditions. However, obtaining both parch location and pixel-level information simultaneously is a challenge in intelligent pavement patch surveys. This paper proposes a deep-learning-based patch instance segmentation network (PISNet) that employs you only look once (YOLO)v5 as the
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Identification of damaged member in a truss structure using acoustic emission technique aided by SVM Struct. Health Monit. (IF 6.6) Pub Date : 2024-04-16 Parikshit Roy, Gudipati Bhanu Kiran, Neetika Saha, Pijush Topdar
Structures are prone to damage, and detecting them at their very initiation is extremely important for taking corrective measures. Truss is a very important civil engineering structure having a complex geometry: width and thickness being very small compared to the length of a member and the presence of discontinuities in the form of joints. Most of the existing techniques identify damages after they
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Spall size estimation for wind turbine pitch bearings: observation, signal processing method and experiments Struct. Health Monit. (IF 6.6) Pub Date : 2024-04-15 Chao Zhang, Long Zhang
It is essential to continuously monitor the spall size of wind turbine pitch bearings to prevent severe faults and catastrophic failure. In the field of spall size estimation for bearings, an essential step is to extract the entry and impact signals simultaneously. And this would become more difficult when it comes to the wind turbine pitch bearings due to the limited fault signals and heavy noise
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Development of data anomaly classification for structural health monitoring based on iterative trimmed loss minimization and human-in-the-loop learning Struct. Health Monit. (IF 6.6) Pub Date : 2024-04-09 Shieh-Kung Huang, Tian-Xun Lin
Huge amounts of data can be generated during long-term monitoring performed by structural health monitoring (SHM) and structural integrity management applications. Monitoring data can be corrupted, and the presence of abnormal data can distort information during signal processing, extract incorrect characteristics during system identification, produce false conclusions during damage detection, and
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Transmission tower bolt-loosening time–frequency analysis and localization method considering time-varying characteristics Struct. Health Monit. (IF 6.6) Pub Date : 2024-04-09 Long Zhao, Guanru Wen, Jingyao Wang, Zhicheng Liu, Xinbo Huang
To address the issues of high concealment and difficult positioning of loose bolts in transmission towers, this paper proposes a new method for locating loose bolts in transmission towers. In this method, we divide the vibration response of the transmission tower into low-frequency signals of 2–25 Hz and high-frequency signals of 25–75 Hz. For the low-frequency signals, the single signal component
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Generalized method for distributed detection and quantification of cracks in bridges Struct. Health Monit. (IF 6.6) Pub Date : 2024-04-06 Chengwei Wang, Maurizio Morgese, Todd Taylor, Mahmoud Etemadi, Farhad Ansari
The development of a generalized machine learning approach based on distributed detection and quantification of cracks by optical fibers is described in this article. A Brillouin scattering optical fiber sensor system was employed to develop, test, and verify the method. The main components of the approach described herein consist of an unsupervised crack identification module based on the iForest
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Phase shift-based resonance assessment for in-service high-speed railway bridges Struct. Health Monit. (IF 6.6) Pub Date : 2024-04-06 Seunghoo Jeong, Hyunmin Kim, Sung Il Kim, Kyoung Chan Lee, Junhwa Lee
Resonance in high-speed railway bridges can deteriorate the structural integrity and running safety of a train; thus, the resonant speed needs to be identified. Previous studies have proposed resonant conditions analytically, but their applications to in-service bridges are limited. Free vibration after the passage of a train was utilized to assess resonance, but it could not capture the natural frequency
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MS-DenseNet-GRU tool wear prediction method based on attention mechanism Struct. Health Monit. (IF 6.6) Pub Date : 2024-04-06 Yaonan Cheng, Jing Xue, Mengda Lu, Shilong Zhou, Xiaoyu Gai, Rui Guan
Tool wear was an inevitable physical phenomenon in the cutting procedure. Serious tool wear has a direct effect on the level of processing quality and the effectiveness of production, and it even leads to abnormal cutting processes and a series of safety problems. Effective tool wear prediction can provide a basis for the rational use and replacement of tools to improve tool efficiency and ensure the
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Damage detection of thin plates by fusing variational mode decomposition and spectral entropy Struct. Health Monit. (IF 6.6) Pub Date : 2024-04-06 Guangtao Lu, Zhiwei Zhou, Longyun Wu, Yangtao Wang, Tao Wang, Dan Yang
This paper presents a new approach for damage detection in thin plates by fusing variational mode decomposition and spectral entropy (VMD-SE). In this method, after the received signal is decomposed into some intrinsic mode functions (IMFs) by variational mode decomposition (VMD), the spectral entropy ratio of the first and last IMFs is calculated for optimizing the VMD’s parameters and improving its
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Monitoring model group of seepage behavior of earth-rock dam based on the mutual information and support vector machine algorithms Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-29 Zhenxiang Jiang
The establishment of a high-precision piezometric water level monitoring model ensures the safe operation of earth-rock dams. The hysteresis effect of the upstream water level and rainfall should be considered during modeling. In the traditional method, the average factors are used to express this effect, and linear regression modeling is adopted. These factors reduce the accuracy of the model. In
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An integrated deep neural network model combining 1D CNN and LSTM for structural health monitoring utilizing multisensor time-series data Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-26 Mohammadreza Ahmadzadeh, Seyed Mehdi Zahrai, Maryam Bitaraf
Introducing deep learning algorithms into the field of structural health monitoring (SHM) has contributed to the automatic extraction of damage-sensitive features, but the type and architecture of these algorithms are still in dispute. This paper proposes a hybrid deep learning framework entitled time-distributed one-dimensional convolutional neural network (1D CNN) long short-term memory (LSTM) model
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Multiscale fluctuation-based symbolic dynamic entropy: a novel entropy method for fault diagnosis of rotating machinery Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-25 Ao Shen, Yongbo Li, Khandaker Noman, Dong Wang, Zhike Peng, Ke Feng
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
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A feature vector with insensitivity to the position of the outer race defect and its application in rolling bearing fault diagnosis Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-25 Jianqun Zhang, Qing Zhang, Wenzong Feng, Xianrong Qin, Yuantao Sun
The fault diagnosis of rolling bearings is very important in industrial applications, which can avoid accidents and reduce operation and maintenance costs. Although the position of the bearing outer race defect has a significant impact on rolling bearing vibration response, most existing intelligent bearing fault diagnosis methods do not take this into account. In this paper, we establish a dynamic
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Advanced deep learning framework for underwater object detection with multibeam forward-looking sonar Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-25 Liangfu Ge, Premjeet Singh, Ayan Sadhu
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
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Deep neural network for damage detection in Infante Dom Henrique bridge using multi-sensor data Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-23 Ana Fernandez-Navamuel, David Pardo, Filipe Magalhães, Diego Zamora-Sánchez, Ángel J Omella, David Garcia-Sanchez
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
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Post-tensioning tendon force estimation of in-service prestressed concrete structure using cylindrical lamb waves Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-23 Ohjun Kwon, Hoon Sohn, Hyung Jin Lim
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
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A method for monitoring the uneven settlement of shield tunnels considering the flattening effect using distributed strain data measured from BOTDA sensors Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-23 Zheng Zhou, Xinteng Ma, Yang Liu, Hu Li
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
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Vision-based detection of bolt tension considering non-rotatory loosening via a new calculation method of bolt flexibility coefficient Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-22 Yong Zhao, Qingyuan Lin, Yuming Liu, Wei Pan
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
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Detecting wire breaks in prestressed concrete pipes: an easy-to-install distributed fibre acoustic sensing approach Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-20 Lisbel Rueda-García, Daniel Tasquer-Val, Pedro Calderón-Bofías, Pedro A Calderón
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
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Multiscale permutation entropy based on natural visibility graph and its application to rolling bearing fault diagnosis Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-19 Ping Ma, Weilong Liang, Hongli Zhang, Cong Wang, Xinkai Li
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
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Structural rotor rub-impact diagnosis under intricate noise interferences based on targeted component extraction and stochastic resonance enhancement Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-18 Yaochun Hou, Huan Wang, Yuxuan Wang, Peng Wu, Wenjun Huang, Dazhuan Wu
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
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Non-contact detection of the interfacial microdefects in metal/CFRP hybrid composites using air-coupled laser ultrasound Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-16 Baoding Wang, Zhongwen Cheng, Weisheng Liao, Bainian Long, Junwei Wu, Lvming Zeng, Xuanrong Ji
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
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Health monitoring of in-cylinder sensors and fuel injectors using an external accelerometer Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-13 Woongsun Jeon, Anastasis Georgiou, Zongxuan Sun, David A Rothamer, Kenneth Kim, Chol-Bum Kweon, Rajesh Rajamani
This paper focuses on the development of a methodology to monitor the health of an engine by detecting any failures in the fuel injectors or in-cylinder pressure sensors using an accelerometer that is non-intrusively mounted on the engine block. A multi-cylinder engine with each cylinder having its own pressure sensor and injector is considered. First, a model relating the combustion component of the
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Early detection of steel tube welded joint failure using SPC-I nonlinear ultrasonic technique Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-13 SeHyuk Park, Imraan Bokhari, Hamad Alnuaimi, Umar Amjad, Robert Fleischman, Tribikram Kundu
Welding is a commonly used method for joining two or more parts together in steel construction. Various defects in weld regions such as cracks, pores, and slag inclusion can be present from the beginning, generated during the welding process, or can be developed while in service. Such defects are the weak spots that degrade the structure’s quality and can lead to structural failures. Therefore, early
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Multi-frequency probabilistic imaging fusion for impact localization on aircraft composite structures Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-12 Deshuang Deng, Xu Zeng, Zhengyan Yang, Yu Yang, Sheng Zhang, Shuyi Ma, Hao Xu, Lei Yang, Zhanjun Wu
Since the internal barely visible damage of aircraft composite structures caused by the impact is a critical problem, impact monitoring is essential for the integrity and reliability of aircraft composite structures. This paper presents a multi-frequency probabilistic imaging fusion method for localizing impacts on aircraft composite structures. To capture the impact signals, a network of distributed
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SEACKgram: a targeted method of optimal demodulation-band selection for compound faults diagnosis of rolling bearing Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-12 Huibin Wang, Changfeng Yan, Yingjie Zhao, Shengqiang Li, Jiadong Meng, Lixiao Wu
Rolling bearing plays an important role in carrying and transmitting power in rotating machinery, and the bearing fault is easy to lead to mechanical accidents, resulting in huge losses and casualties. Therefore, the condition monitoring and diagnosis of rolling bearings are very important to improve the safety of equipment. Compound fault is a common fault evolved from the initial defect, which is
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Damage detection and location using a simulated annealing-artificial hummingbird algorithm with an improved objective function Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-12 Zhen Chen, Yikai Wang, Kun Zhang, Tommy HT Chan, Zhihao Wang
Swarm intelligence algorithms and finite element model update technology are important issues in the field of structural damage detection. However, the complexity of engineering structural models normally leads to low computational efficiency and large detection errors in structural damage detection. To solve these problems, a simulated annealing-artificial hummingbird algorithm (SA-AHA) is proposed
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Multi-objective SHM sensor path optimisation for damage detection in large composite stiffened panels Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-12 Llewellyn Morse, Ilias N Giannakeas, Vincenzo Mallardo, Zahra Sharif-Khodaei, MH Aliabadi
This work proposes a novel methodology for the automatic multi-objective optimisation of sensor paths in structural health monitoring (SHM) sensor networks using archived multi-objective simulated annealing. Using all of the sensor paths within a sensor network may not always be beneficial during damage detection. Many sensor paths may experience significant signal noise, attenuation, and wave mode
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Vibrational resonance mechanism in the high-order-degradation bistable system and its application in fault diagnosis Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-11 Haitao Xu, Shengxi Zhou
Bearings play an important role in the rotating machinery. Timely fault detection and maintenance can prevent catastrophic incidents caused by bearing faults. As one of the advanced techniques to extract the weak characteristics of bearing fault, the methods based on the vibrational resonance (VR) mechanism can effectively amplify the weak characteristics. However, first, the effect of the barrier
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Bidirectional graphics-based digital twin framework for quantifying seismic damage of structures using deep learning networks Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-08 Guanghao Zhai, Yongjia Xu, Billie F. Spencer
Tremendous effort has been devoted toward developing automated post-earthquake inspection techniques, including automated image collection and damage identification. However, few studies have attempted to establish the complex relationship between visible damage and structural conditions. Moreover, the lack of training data further hinders the potential use of deep learning algorithms. This paper proposes
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Thermo-oxidative aging state detection of rubber sandwich structure using synchrosqueezing transform-assisted feature extraction and customized detection indicator Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-07 Xujun Zhao, Ye Tian, Dalong Han, Yue Si, Meng Zhang, Liandi He
Rubber sandwich structures (RSSs) are used extensively in mechanical engineering. The aging state detection of such structures is urgently required to avoid disastrous accidents. However, this is still a challenging task owing to the weakness of the rubber layer aging feature information contained in the vibration signal of the RSS and the lack of effective aging feature information extraction techniques
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Defect detection and localisation using guided wave images from array data processed by nonlinear autoregressive exogenous model and Gamma statistical operator Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-07 Kangwei Wang, Jie Zhang, Yang Xiao, Anthony J. Croxford, Yong Yang
Guided wave structural health monitoring (GWSHM) systems, using the delay-and-sum imaging algorithm, are an efficient solution to detect and localise defects in industrial structures. However, the image artifacts caused by either imperfect detection or sensor lay-out limitations make it difficult to identify and locate defects accurately. In order to enhance the performance of defect detection and
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Interpretable Siamese dual attention enhancement transfer compound diagnostic model for unbalanced samples Struct. Health Monit. (IF 6.6) Pub Date : 2024-03-07 Kun Xu, Shunming Li, Xiaodong Miao, Hua Wang, Ranran Li
The intelligent transfer diagnosis model is used to address the issue of feature drift caused by the changing working conditions of rotating parts in engineering. However, few models can perform transfer diagnosis on multiple unbalanced samples of rotating parts simultaneously, and even fewer models can visually enhance the domain-invariant features, making them more interpretable. To address these
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Unsupervised deep learning approach for structural anomaly detection using probabilistic features Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-29 Hua-Ping Wan, Yi-Kai Zhu, Yaozhi Luo, Michael D Todd
Civil structures may deteriorate during their service life due to degradation or damage imposed by natural hazards such as earthquakes, wind, and impact. Structural performance anomaly detection is essential to provide an early warning of structural degradation limit states in order to prevent potential catastrophic failure. Data-driven machine learning approaches have been widely used for this, due
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Vibration-based structural damage localization through a discriminant analysis strategy with cepstral coefficients Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-28 Lechen Li, Adrian Brügger, Raimondo Betti, Zhenzhong Shen, Lei Gan
Over the past decades, Vibration-Based Methods (VBMs) have consistently exhibited exceptional effectiveness in the field of Structural Health Monitoring when it comes to assessing structural damage in both civil and mechanical structures. Recently, the progress made in data-driven strategies for localizing structural damage through the VBMs has resulted in substantial benefits. These advanced strategies
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Gradient-based domain-augmented meta-learning single-domain generalization for fault diagnosis under variable operating conditions Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-28 Chuanxia Jian, Heen Chen, Chaobin Zhong, Yinhui Ao, Guopeng Mo
Equipment operating conditions, referred to as domains, can induce domain drift in monitoring data, affecting data-driven fault diagnosis. Researchers have explored multi-domain generalization methods to tackle this issue. However, in actual industrial scenarios, the availability of fault data may be limited to a specific condition due to the cost or feasibility constraints associated with collecting
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Application of covariance statistical method for damage identification on railway truss bridge using acceleration response: experimental and numerical validation Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-27 Md. Arif Faridi, Koushik Roy, Vaibhav Singhal
This paper presents a novel statistical analysis-based approach to non-parametric damage detection in truss bridges. The method utilizes the normalized acceleration response time histories (NARTHs) of a bridge under random excitation. The coefficients of variation matrices are calculated using NARTHs for the truss bridge in both its baseline and damaged states. The results are shown as the difference
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A novel image multitasking enhancement model for underwater crack detection Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-27 Wenxuan Cao, Junjie Li
Remotely Operated Vehicles (ROVs) carrying vision systems provide an efficient solution for the underwater crack search. However, the degradation of underwater images severely limits the prognosis of cracks. For the problem of ROV image multiple degradation in complex underwater environments, a robust and accurate multitask enhancement method for underwater crack images is proposed, which can simultaneously
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Noncontact geomagnetic defect localization of buried energy pipelines using ICEEMDAN approach with MVF Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-27 Zia Ullah, Kong Fah Tee
Structural assessment of buried energy pipelines is often hindered by the abundance of external vibrations resulting in nebulous noises. Effective and secure nondestructive approaches need to be devised to efficiently reduce noise in multidimensional magnetic anomaly signals collected from a pipeline. This study focuses on the mechanism by which a measured source signal can be broken down into low-
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Deep exponential excitation networks: toward stronger attention mechanism for weak fault diagnosis Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-26 Baihong Zhong, Minghang Zhao, Shisheng Zhong, Lin Lin, Yongjian Zhang
Considering that large mechanical equipment often has various excitation sources, the signals generated by these excitation sources are often not simply added or multiplied together, but nonlinearly mixed, which exhibit complex non-stationary characteristics, making classical algorithms difficult to extract fault features. Especially when faults just occur, the fault symptom is often weak and submerged
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A two-level fusion model of vibro-acoustic signals for centrifugal fan blade crack detection Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-23 Tianchi Ma, Junxian Shen, Di Song, Feiyun Xu
Blade crack detection is the key to ensuring the smooth and safe operation of centrifugal fans. However, a single vibration signal is difficult to fully reflect the health state of the blade and is susceptible to noise interference in the industrial field, which makes it difficult to detect blade cracks. Therefore, a two-level fusion model of vibro-acoustic signals is proposed for blade crack detection
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Unsupervised damage assessment under varying ambient temperature based on an adjusted artificial neural network and new multivariate covariance-based distances Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-23 Ali Nikdel, Hashem Shariatmadar
Temperature variability is one of the critical environmental conditions that causes confusing changes in structural properties and dynamic responses of bridges similar to damage. In this case, false alarms and mis-detection are among the major errors in health monitoring of such civil structures. High damage detectability is another significant challenge in bridge health monitoring. To deal with these
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A simple image correlation technique for imaging subsurface damage from low-velocity impacts in composite structures Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-22 T. Bryce Abbott, Fuh-Gwo Yuan
A robust computer vision system is proposed to visualize subsurface barely visible impact damage (BVID) in composite structures through a simple image correlation technique together with a damage imaging condition. This system uses a digital camera to record a video of the surface motion, capturing micron-scale dynamic movement from guided waves propagating on the surface of the structure generated
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Automatic pipeline fault detection using one-dimensional convolutional bidirectional long short-term memory networks with wide first-layer kernels Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-22 Longguang Peng, Wenjie Huang, Guofeng Du, Yuanqi Li, Qiqi Xu, Kai Zhou, Jicheng Zhang
Pipeline networks are crucial components of modern infrastructure, and ensuring their reliable operation is essential for sustainable development. The percussion-based methods are considered promising for detecting pipeline faults due to their avoidance of constant-contact sensors and ease of implementation. However, the majority of existing percussion-based methods suffer from limitations such as
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Weakly supervised crack segmentation using crack attention networks on concrete structures Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-17 Anoop Mishra, Gopinath Gangisetti, Yashar Eftekhar Azam, Deepak Khazanchi
Crack detection or segmentation on concrete structures is a vital process in structural health monitoring (SHM). Though supervised machine learning techniques have gained tremendous success in this domain, data collection and annotation continue to be challenging. Image data collection is challenging, tedious, and laborious, including accessing representative datasets and manually labeling training
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Semantic segmentation model for concrete cracks based on parallel Swin-CNNs framework Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-17 Xiaojian Han, Junwen Zheng, Lingkun Chen, Qizhi Chen, Xiaoming Huang
In recent years, crack detection has been the focus of relevant research since concrete fractures are the most dangerous damage to structures. Computer vision-based approaches are frequently employed for their distinct benefits. However, the crack segmentation model based only on convolutional neural networks (CNNs) is still inadequate in generalization because of its inherent bias produced by its
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Strategy for vertical deformation of railway bridge monitoring using polarimetric ground-based real aperture radar system Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-14 Lilong Zou, Giovanni Nico, Amir Morteza Alani, Motoyuki Sato
The health monitoring of infrastructure is vital for ensuring the safety and structural integrity of bridges. Recently, ground-based real aperture radar (GB-RAR) systems have been successfully utilized in the dynamic and static monitoring of bridges. In this study, a comprehensive and innovative approach is presented to monitor the vertical deformation of a long-span metallic railway bridge and a reinforced
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Probability of detection for corrosion-induced steel mass loss using Fe–C coated LPFG sensors Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-14 Ying Zhuo, Pengfei Ma, Chuanrui Guo, Genda Chen
The traditional probability of detection (POD) method, as described in the Department of Defense Handbook MIL-HDBK-1823A for nondestructive evaluation systems, does not take the time dependency of data collection into account. When applied to in situ sensors for the measurement of flaw sizes, such as fatigue-induced crack length and corrosion-induced mass loss, the validity and reliability of the traditional
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Dynamic behavior of large faults toward severity estimation in bearings Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-14 Eyal Madar, Alon Sol, Renata Klein, Jacob Bortman
Estimation of fault severity throughout bearing life is required for bearing prognostics and remaining useful life estimation. The useful life limit varies based on the function and criticality of the bearing and the machine. Most research has focused on the initial stages when faults are small. This paper presents novel severity categories of a spall-like fault located on a deep groove bearing outer
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On the limitations of transmissibility functions for damage localisation: the influence of completeness Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-14 Joshua WR Meggitt, Ramin C McGee
Transmissibility functions are used to identify and locate damage in critical structures for health monitoring purposes. Their appeal over conventional signal or frequency response-based functions lie in a unique property; sub-structural invariance. It has been shown that the transmissibility of an assembled structure, when obtained correctly, can describe the dynamics of a sub-structure in a manner
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Optimization of distributed fiber optic sensors for pavements by combining DEM-FDM coupled numerical simulation method and response surface method Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-14 Zejiao Dong, Jiwen Zhang, Xianyong Ma, Yongkang Dong, Donghao Wang, Yiheng Li
The cooperative deformability of sensors embedded into a host material and the measurement accuracy of these sensors affect pavement health monitoring. In consideration of these concerns, this study combined numerical simulation and the response surface method to optimize sensor design and standardize the distributed fiber optic sensors (DFOSs) used in pavement engineering. Numerical models were developed
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Particle filter-based fatigue damage prognosis by fusing multiple degradation models Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-12 Tianzhi Li, Jian Chen, Shenfang Yuan, Dimitrios Zarouchas, Claudio Sbarufatti, Francesco Cadini
Fatigue damage prognosis always requires a degradation model describing the damage evolution with time; thus, the prognostic performance highly depends on the selection of such a model. The best model should probably be case specific, calling for the fusion of multiple degradation models for a robust prognosis. In this context, this paper proposes a scheme of online fusing multiple models in a particle
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Automatic high-precision crack detection of post-earthquake structure based on self-supervised transfer learning method and SegCrackFormer Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-12 Shiqiao Meng, Ying Zhou, Abouzar Jafari
Accurate crack detection is essential for structural damage assessment after earthquake disasters. However, due to the gap between the target domain of the detected structure and the source domain, it is challenging to achieve high-precision crack segmentation when performing crack detection based on deep learning (DL) in actual engineering. This article proposes a crack segmentation transfer learning
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Quantitative monitoring of icing on CFRP laminate with guided wave combining forward modeling and inverse characterization Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-12 Yuan Tian, Anchalee Duongthipthewa, Qi Chen, Haotian Guo, Menglong Liu, Jifeng Zhang, Limin Zhou
Aircraft icing is one of the critical factors for flight safety. Timely and accurate monitoring of in-flight ice accretion is essential for flight systems to take immediate and effective action to significantly improve the efficiency of subsequent de-icing processes and ensure flight safety. Since carbon fiber reinforced polymer (CFRP) are now widely used in the aviation industry, the study of ice
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Multi-source domain adversarial graph convolutional networks for rolling mill health states diagnosis under variable working conditions Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-12 Shuai Zhao, Leping Bao, Changhui Hou, Yang Bai, Yue Yu
As the rolling mill often encounters variable and complicated working conditions and shock loads, unsupervised domain adaptive (UDA) methods are imperative in its health monitoring. However, efforts of applying UDA methods on the rolling mill are negligible, and many existing approaches have constraints in domain adaptation, domain label, and data construction that prevent meaningful features from
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A miniaturized passive wireless patch antenna sensor for structural crack sensing Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-12 Xianzhi Li, Songtao Xue, Liyu Xie, Guochun Wan
This paper presents a miniaturized patch antenna sensor for structural crack sensing, and the patch antenna sensor can be interrogated wirelessly. The proposed patch antenna sensor can detect the expansion of structural crack through relative movement between antenna components, and the relationships between the antenna resonant frequency and the crack width were studied. By using Rogers RO3010 laminate
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Intelligent fault diagnosis of storage stacking machinery under variable working conditions using attention-based adaptive multimodal feature fusion networks Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-12 Xiangyin Meng, Yang Li, Xinxin Xie, Zhicheng Peng, Shichu Li, Lei Xie, Huiping Huang, Jian Zhang, Peng Guo, Min Zhang, Shide Xiao
Due to the harsh working environment of storage stacking machinery, the fault information of important components is significantly complex, which leads to the problem of low classification accuracy and high computational complexity of existing deep learning-based fault diagnosis methods. To alleviate the problem, this paper presents a novel architecture named attention-based adaptive multimodal feature
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Multibolt looseness monitoring of steel structure based on multitask active sensing method and substructure cross-domain transfer learning Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-12 Yixuan Chen, Jingyi Wei, Zhennan Gao, Weijie Li, Jianchao Wu
Under the influence of service life and the external environment, bolted connections are prone to loosening, which may lead to structural hazards. Thus, it is crucial to carry out real-time monitoring of bolted connections. Based on the active sensing method, previous researchers mainly focused on quantifying the single-bolt looseness, with little focus on locating and quantifying the multibolted connection
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An adaptable rotated bounding box method for automatic detection of arbitrary-oriented cracks Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-12 Yonghui An, Lingxue Kong, Chuanchuan Hou, Jinping Ou
Concrete crack detection is a crucial task for the safety and durability of engineering structures. Extensive research has been conducted on deep-learning methods employing horizontal bounding boxes (HBBs) for crack detection. However, due to the inherently random distribution of concrete cracks, HBB-based methods often produce excessive overlaps and encompass extensive background regions, obstructing
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Probabilistic State Estimation Under Varying Loading States via the Integration of Time-Varying Autoregressive and Gaussian Process Models Struct. Health Monit. (IF 6.6) Pub Date : 2024-02-12 Ahmad Amer, Shabbir Ahmed, Fotis Kopsaftopoulos
In this work, probabilistic damage quantification under varying loading conditions in a non-stationary, guided-wave environment is being tackled via the synergistic integration between Time-varying Autoregressive (TAR) models and Gaussian Process regression models (GPRMs). Applying these TAR-GPRMs onto an aluminum coupon with simulated damage under different loading conditions fitted with piezoelectric