Structural Health Monitoring最新文献

{"title":"Resilient fast nonlinear blind deconvolution with uniform initialization for the bearing fault diagnosis","authors":"Hao Ma, Baokun Han, Jinrui Wang, Zongzhen Zhang, Huaiqian Bao","doi":"10.1177/14759217241268914","DOIUrl":"https://doi.org/10.1177/14759217241268914","url":null,"abstract":"[Formula: see text] The extraction of defective bearing feature under sudden load variations and mutual interference among components is a challenging task. The key is to overcome the strong background noise and random shock disturbances. Fast nonlinear blind deconvolution (FNBD) with superior noise adaptability is considered as a powerful tool to tackle the challenge. However, the reliability of FNBD is reduced by misdiagnosis under random shock interference and computational instability. In addition, extraction performance of FNBD is affected by the setting of complex parameters. To address above issues and broaden the applicability of FNBD, resilient fast nonlinear blind deconvolution (RFNBD) is proposed. First, the impact of filter initialization on the extraction accuracy and stability of FNBD is studied. The results indicate that the FNBD converges to components in the signal that are close to the center frequency of the initial filter, and the robustness of FNBD is limited by the original initialization mode. Based on this, a novel initialization pattern is proposed to improve the robustness under random shock interference and computational stability. Subsequently, the inferior filter elimination strategy is introduced to enhance the extraction efficiency and intelligence of RFNBD. Finally, the superior robustness under variable parameters and extraction performance under strong interference of RFNBD is demonstrated by simulation and experiment. In the XJTU-SY datasets, the proposed RFNBD extracted fault characteristic frequency and its first four harmonics from 16,384 sampling points 11 min earlier than the traditional method.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925612","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":"Pitting corrosion diagnostics and prognostics for miter gates using multiscale simulation and image inspection data","authors":"Gu Qian, Zihan Wu, Zhen Hu, Michael D. Todd","doi":"10.1177/14759217241264291","DOIUrl":"https://doi.org/10.1177/14759217241264291","url":null,"abstract":"Physics-based high-fidelity pitting corrosion simulation models have successfully predicted the evolution of corrosion pit morphology for given mechanical and environmental conditions. However, applying such models for pitting corrosion diagnostics and prognostics in large civil infrastructures such as found in the inland waterways navigation enterprise is very challenging, primarily due to the impracticality of measuring individual pits. This paper addresses this challenge by bridging the gap between physics-based pitting corrosion simulation and vision-based pitting corrosion inspection of large civil infrastructures. The framework proposed in this paper consists of four main modules: mesoscale pitting corrosion simulation using the phase-field method, macroscale structural analysis, pitting corrosion detection using machine learning, and updating physics-based simulation models based on pitting corrosion detection. It begins with developing a forward simulation framework to predict the evolution of pitting corrosion on large civil infrastructure using multiscale analysis. A convolutional neural network (CNN)-based pit detection method is created in parallel to autonomously identify and extract pitting corrosion observations from corrosion inspection images. Finally, an approximate Bayesian computation numerical framework is proposed to update three key model parameters in the forward pitting corrosion simulation model using the detection results from the trained CNN model. The updated multiscale simulation model can then be used for pitting corrosion prognostics. A practical application example is demonstrated on miter gates to show the effectiveness of the proposed framework.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"8 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797853","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":"3DGEN: a framework for generating custom-made synthetic 3D datasets for civil structure health monitoring","authors":"Yanda Shao, Ling Li, Jun Li, Qilin Li, Senjian An, Hong Hao","doi":"10.1177/14759217241265540","DOIUrl":"https://doi.org/10.1177/14759217241265540","url":null,"abstract":"The availability of high-quality datasets is increasingly critical in the field of computer vision-based civil structural health monitoring, where deep learning approaches have gained prominence. However, the lack of specialized datasets for such tasks poses a significant challenge for training a reliable model. To address this challenge, a framework, 3DGEN, is proposed to swiftly generate realistic synthetic 3D datasets which can be targeted for specific tasks. The framework is based on diverse 3D civil structural models, rendering them from various angles and providing depth information and camera parameters for training neural networks. By employing mathematical methods, such as analytical solutions and/or numerical simulations, deformation of civil engineering structures can be generated, ensuring a reliable representation of their real-world shapes and characteristics in the 3D datasets. For texture generation, a generative 3D texturing method enables users to specify desired textures using plain English sentences. Two successful experiments are conducted to (1) assess the efficiency of generating the 3D datasets using two distinct structures, (2) train a monocular depth estimation network to perform 3D surface reconstruction with the generated dataset. Notably, 3DGEN is not limited to 3D surface reconstruction; it can also be used for training neural networks for various other tasks. The code and dataset are available at: https://github.com/YANDA-SHAO/Beam-Dataset-SE","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"4 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798127","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 passive-to-active fusion method using neural network for structural damage localization under workload","authors":"Xuyun Ding, Honggang Cheng, Xiaojun Wang, Pengfei Wu, Xiaofeng Sun, Ke Wan","doi":"10.1177/14759217241256677","DOIUrl":"https://doi.org/10.1177/14759217241256677","url":null,"abstract":"Advanced aircraft structures are susceptible to hazardous factors such as external impact while in operation. It is crucial to establish aircraft health-monitoring technology that enables online safety status evaluation of composite structures. However, the problem of low accuracy in structural damage localization under working load persists. This study proposes a progressive research methodology that employs the innovative idea of feature-level fusion. The methodology involves active guided wave mechanism analysis, guided wave feature extraction, adaptive compensation, and precise damage localization. An improved active damage localization method oriented by passive real-time strain sensing is proposed. Verification and validation experiments fully verify the feasibility, applicability, and accuracy of the method, achieving damage localization under working load. In essence, through passive to active mapping network at its core, this study has to some extent overcome the bottleneck problem of aircraft damage localization that is unreliable under working load.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797037","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":"Innovative computer vision-based full-scale timber element cracks detection, stitching, and quantification","authors":"Yewei Ding, Haibei Xiong, Lin Chen, Jiawei Chen, Jia Xu, Xiaoming Qin, Jiaxuan Gu","doi":"10.1177/14759217241258682","DOIUrl":"https://doi.org/10.1177/14759217241258682","url":null,"abstract":"Timber is susceptible to environmental humidity variations, inevitably resulting in cracks parallel to the wood grain during the service life. Cracks significantly degrade the effective cross-sectional area and seriously affect structural safety and durability. Therefore, it is significant to identify the timber elements’ cracking conditions for providing reliable maintenance. Existing timber structure crack inspection mainly relies on manual work. However, with the rapid development of high-rise and large-span glued timber structure, manual-based crack inspection is not applicable to such structures for increasing workload and uncontactable high-altitude timber elements. In order to make up for the deficiencies of the existing crack detection algorithms, this paper proposed an innovative computer vision-based method inspecting full-scale timber column cracks. In step one, the crack images were stitched to exhibit the full-scale cracking condition. In step two, the YOLOv5 model was trained utilizing 425 images collected from cracked timber structures and performed K-fold crossover validation algorithm. In step three, cracking regions are quantified at the physical level. Field tests showed that the proposed method has a crack identification precision better than 0.2 mm and error below 5% compared with manual measurement, which can provide high-precision, time-saving, and noncontact in-situ crack inspection for timber structures.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797968","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 recurrent-convolutional neural network learning and physics Kalman filtering comparison in dynamic load identification","authors":"Marios Impraimakis","doi":"10.1177/14759217241262972","DOIUrl":"https://doi.org/10.1177/14759217241262972","url":null,"abstract":"The dynamic structural load identification capabilities of the gated recurrent unit, long short-term memory, and convolutional neural networks are examined herein. The examination is on realistic small dataset training conditions and on a comparative view to the physics-based residual Kalman filter (RKF). The dynamic load identification suffers from the uncertainty related to obtaining poor predictions when in civil engineering applications only a low number of tests are performed or are available, or when the structural model is unidentifiable. In considering the methods, first, a simulated structure is investigated under a shaker excitation at the top floor. Second, a building in California is investigated under seismic base excitation, which results in loading for all degrees of freedom. Finally, the International Association for Structural Control-American Society of Civil Engineers (IASC-ASCE) structural health monitoring benchmark problem is examined for impact and instant loading conditions. Importantly, the methods are shown to outperform each other on different loading scenarios, while the RKF is shown to outperform the networks in physically parametrized identifiable cases.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"86 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798450","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":"Effective pre-stress identification in steel strand based on ultrasonic guided wave and 1-dimensional convolutional neural network","authors":"Longguan Zhang, Junfeng Jia, Yulei Bai, Xiuli Du, Binli Guo, He Guo","doi":"10.1177/14759217241263955","DOIUrl":"https://doi.org/10.1177/14759217241263955","url":null,"abstract":"The accurate assessment of the effective pre-stress in steel strands is a challenging task, and ultrasonic guided wave (UGW) technique has shown certain application prospects in this field. However, the existing UGW-based approaches require manual parameter extraction from signals in time domain or frequency domain, which is a cumbersome and time-consuming process, and pre-stress identification based on individual parameters may not be reasonable. This study proposes a framework for identifying effective pre-stress in steel strands based on UGW and one-dimensional convolutional neural network (1D-CNN), which does not require any parameter extraction operation and achieves high identification accuracy. The output features of various convolutional layers in 1D-CNN were downscaled and visualized, and the prediction results of 1D-CNN were compared with those of a support vector regression (SVR) model. Results show that with the deepening of the network, the correlation between output features of the convolutional layers and pre-stress values increases significantly, indicating that the 1D-CNN model is able to automatically extract features related to the variation of pre-stress. The pre-stress prediction accuracy using 1D-CNN is significantly higher than that using SVR, and the prediction error is within 3%. The proposed 1D-CNN model exhibits excellent noise-robustness, with the prediction error remaining within 10% even at the SNR level of −5 dB. Even after removing half of conditions in the training set, the proposed 1D-CNN model is still able to achieve accurate identification of effective pre-stress.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"5 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797354","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":"Acoustic signals analysis from an innovative UAV inspection system for wind turbines","authors":"Pedro José Bernalte Sánchez, Isaac Segovia Ramírez, Fausto Pedro García Márquez, Alberto Pliego Marugán","doi":"10.1177/14759217241262970","DOIUrl":"https://doi.org/10.1177/14759217241262970","url":null,"abstract":"Wind energy is emerging as a leading renewable energy source, with the deployment of large and more innovative wind turbines (WTs). This expansion requires new condition monitoring systems (CMS) and diagnostic techniques to reach competitiveness, improve reliability and availability, and minimize maintenance costs associated with WT operations. This research proposes a novel CMS based on acoustic analysis of WTs, combined with advanced analytics for pattern recognition. An acoustic CMS embedded in an unmanned aerial vehicle is developed to capture, send, and process the sound emitted in the nacelle to an acoustic receiver by a ground station for further analysis to assess the viability of the methodology. The article presents initial results from the laboratory using the fast Fourier transform algorithm, studying the signals in the time-frequency domain aspect and measuring the energy. An advanced signal processing method is presented to filter and define patterns that identify the state and condition of different proposed scenarios. The methodology is tested in a working WT, and the results demonstrate that the acoustic analysis is suitable for maintenance management in WTs.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"7 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797370","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 likelihood-free Bayesian approach for characterisation of multiple delaminations in laminated composite beams","authors":"Zijie Zeng, Yuan Feng, C. Ng, Abdul H. Sheikh","doi":"10.1177/14759217241256395","DOIUrl":"https://doi.org/10.1177/14759217241256395","url":null,"abstract":"This paper presents a probabilistic model-based optimisation framework for localising and characterising multiple delaminations in laminated composite beams using ultrasonic guided waves (GW). A likelihood-free Bayesian method, approximate Bayesian computation by subset simulation (ABC-SS), is implemented to determine the number of delaminations and identify the unknown damage characteristics and their associated uncertainties. The ABC algorithm provides a practical way to approximate the posterior distributions of uncertain damage parameters and select the most plausible damage model for determining the number of delaminations through direct comparison of the experimentally measured and numerically simulated GW signals without assuming any likelihood functions. To overcome the expensive computational cost of traditional finite element simulations, a higher-order laminated model (HOLM) is employed to model the GW propagation behaviour in the delaminated composite beams with satisfactory accuracy and acceptable computational efficiency. Benefiting from the accurate simulation, the dataset comparison utilises the original time-domain GW signals, thereby preventing the loss of any key information from the signals for damage identification. A comprehensive series of numerical case studies are used to demonstrate the accuracy, robustness and feasibility of HOLM simulation and the proposed multiple-delamination identification framework. The practicability and accuracy of the proposed ABC framework are further validated using two experimental datasets.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"47 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798980","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":"Detection and time-of-arrival estimation of impact-induced waves in composite laminates","authors":"Lukas Grasboeck, Alexander Humer, A. Benjeddou","doi":"10.1177/14759217241260848","DOIUrl":"https://doi.org/10.1177/14759217241260848","url":null,"abstract":"This work assesses experimentally three selected methods for estimating the time of arrival (TOA) of guided waves generated by impacts on a carbon fiber reinforced polymer plate-like structure. This is reached by the individual evaluation of the continuous wavelet transform (CWT), the modified energy ratio (MER), and the Akaike information criterion (AIC). Considering the anisotropic nature of the laminated structure, the propagation of guided waves within such thin-walled structure presents intricate challenges. The utilization of surface-bonded piezoceramic patch sensors aids to detect and measure wave propagation across the plate. The experiments involved impacting the plate at various positions using an impulse hammer and an impact gun, generating a comprehensive dataset encompassing signals captured by sensors placed at different locations. Our investigations have revealed that, with specific modifications, these methods offer effective means of estimating the TOA. In the CWT framework, the introduced frequency domain threshold crossing technique enhances precision in the TOA estimation. To address the slight delays in TOA estimations using the MER method, we refined the method by identifying the first local maximum that exceeds a predefined threshold. This enhancement significantly improves the method’s ability to estimate the TOA of sensor signals. Among the explored methods, the AIC stands out for its precise detection of initial impact-induced signal alterations. This method consists of two sequential steps, with the second step notably influencing its outcome. By incorporating local minima from the method-specific function, instead of relying solely on the global minimum, a significant improvement in estimation accuracy was observed in several cases. Nevertheless, our modification led to notably inaccurate estimates in certain cases, indicating instances where the estimates were significantly too early. These findings offer valuable insights into successful TOA estimation, facilitating potential applications such as impact localization and guiding further research in this domain.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"50 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803833","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}