Structural Health Monitoring-An International Journal最新文献_第9页

Dynamical modeling of spur gear with pitting based on image processing tooth surface 基于图像处理的齿面点蚀直齿轮动力学建模

IF 6.6 2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-04-24 DOI: 10.1177/14759217231169209

Wenzheng Liu, R. Zhu, Xudong Song, Wenguang Zhou, Jingjing Wang

引用次数: 0

Crack identification using smart paint and machine learning 使用智能油漆和机器学习进行裂缝识别

IF 6.6 2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-04-24 DOI: 10.1177/14759217231167823

Said Quqa, Sijia Li, Yening Shu, L. Landi, K. Loh

{"title":"Crack identification using smart paint and machine learning","authors":"Said Quqa, Sijia Li, Yening Shu, L. Landi, K. Loh","doi":"10.1177/14759217231167823","DOIUrl":"https://doi.org/10.1177/14759217231167823","url":null,"abstract":"Information on the presence and location of cracks in civil structures can be precious to support operators in making decisions related to structural management and scheduling informed maintenance. This paper investigates the efficacy of supervised machine learning to solve the inverse electrical impedance tomography problem and to reconstruct the conductivity distribution of a piezoresistive sensing film. This film consists of a conductive paint applied onto structural components, and operators can use its conductivity distribution to identify crack sizes and locations in the underlying structure. A deep neural network is employed to reconstruct a dense conductivity distribution within the painted area by using only voltage measurements collected at sparse boundary locations. Since one of the most challenging aspects of using supervised learning tools for real-world applications is generating a representative training dataset, this paper presents a new approach to test the suitability of synthetic datasets built using a finite element model of the sensing film. Results are reported for four sensing specimens fabricated with two different techniques (i.e., using carbon nanotubes and graphene nanosheets, respectively). Crack-like damage is induced to the substrate of the sensing film and identified using the proposed machine learning technique. Promising results are obtained as compared to conventional methods.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42692730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Dual generative adversarial networks combining conditional assistance and feature enhancement for imbalanced fault diagnosis 结合条件辅助和特征增强的双生成对抗网络用于不平衡故障诊断

IF 6.6 2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-04-24 DOI: 10.1177/14759217231165223

Ranran Li, Shunming Li, Kun Xu, Mengjie Zeng, Xianglian Li

{"title":"Dual generative adversarial networks combining conditional assistance and feature enhancement for imbalanced fault diagnosis","authors":"Ranran Li, Shunming Li, Kun Xu, Mengjie Zeng, Xianglian Li","doi":"10.1177/14759217231165223","DOIUrl":"https://doi.org/10.1177/14759217231165223","url":null,"abstract":"The dataset in the application scenario of existing fault diagnosis methods is often balanced, while the data collected under actual working conditions are often imbalanced. Directly applying existing fault diagnosis methods to this scenario will lead to poor diagnosis effect. In view of the above problems, we proposed a method called dual generative adversarial networks (DGANs) combining conditional assistance and feature enhancement. The method uses data augmentation as a basic strategy to supplement imbalanced datasets by generating high-quality data. Firstly, a new generator is designed to build the basic framework by sharing the dual-branch deconvolutional neural networks, and combining the label auxiliary information and the coral distance loss function to ensure the diversity of generated samples. Secondly, a new discriminator was designed, which is based on deep convolutional neural networks and embedded with auxiliary classifiers, further expanding the function of the discriminator. Thirdly, the self-attention module is introduced into both the generator and the discriminator to enhance deep feature learning and improve the quality of generated samples; finally, the proposed method is experimentally validated on datasets of two different testbeds. The experimental results show that the proposed method can generate fake samples with rich diversity and high quality, using these samples to supplement the imbalanced dataset, the effect of imbalanced fault diagnosis has been substantially improved. This method can be used to solve the problem of fault diagnosis in the case of sample imbalance, which often exists in actual working conditions.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42753209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Drive-by infrastructure monitoring: a workflow for rigorous deformation analysis of mobile laser scanning data 行车基础设施监控:移动激光扫描数据的严格变形分析工作流程

IF 6.6 2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-04-17 DOI: 10.1177/14759217231168997

Slaven Kalenjuk, W. Lienhart

{"title":"Drive-by infrastructure monitoring: a workflow for rigorous deformation analysis of mobile laser scanning data","authors":"Slaven Kalenjuk, W. Lienhart","doi":"10.1177/14759217231168997","DOIUrl":"https://doi.org/10.1177/14759217231168997","url":null,"abstract":"This paper presents a practical and efficient workflow for deformation monitoring of transport infrastructure. We propose using commercially available mobile laser scanning (MLS) systems to scan civil infrastructure while driving by in a car or rail vehicle. Our processing pipeline corrects for MLS-specific systematic deviations and models deformations from point clouds of two epochs. Following the concept of rigorous deformation analysis, we statistically test the deformations for significance. The required point cloud uncertainty may be obtained in two ways. First option is empirically by multiple passes and, secondly, by prediction with a learned stochastic model. We apply the method to three retaining structures and evaluate results based on ground truth geodetic surveys. The deviations did not exceed 10 mm, even for complex object surfaces or when traveling at 80 km/h. We demonstrate that the method is capable of revealing displacements in the centimeter range without relying on any installations on the structure. The approach shows great potential as a novel, efficient tool for detecting and quantifying defective structures in a road and railway network.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41555962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Continuous monitoring of rolling element bearing health by nonlinear weighted squared envelope-based fuzzy entropy 基于非线性加权平方包络模糊熵的滚动轴承健康连续监测

IF 6.6 2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-04-17 DOI: 10.1177/14759217231163090

Khandaker Noman, Yongbo Li, G. Wen, A. U. Patwari, Shun Wang

{"title":"Continuous monitoring of rolling element bearing health by nonlinear weighted squared envelope-based fuzzy entropy","authors":"Khandaker Noman, Yongbo Li, G. Wen, A. U. Patwari, Shun Wang","doi":"10.1177/14759217231163090","DOIUrl":"https://doi.org/10.1177/14759217231163090","url":null,"abstract":"Fuzzy entropy (FE) can be regarded as an effective measure for nonlinear characterization of rolling element bearing (REB) health condition by quantifying the complexity of vibration signals. However, during continuous monitoring operation under heavy noise, transient impulses corresponding to a REB fault get submerged under unnecessary random noise components. As a consequence, FE algorithm not only fails to detect a REB fault at the earliest point of inception but also performs poorly in monitoring the development of the incepted fault in an efficient manner. Aiming at solving the aforementioned limitations of FE in continuous monitoring of REB health, background noise associated with collected vibration signals is eliminated by weighting the corresponding square envelope signal. Due to the utilization of weighted squared envelope signal, the proposed measure is termed as weighted square envelope-based FE (WSEFE). One simulated case and two different run-to-failure experimental cases are used for validation. The comparison results demonstrate that the proposed WSEFE not only overcomes the limitations of original FE but also performs better than conventional permutation entropy and advanced FE-based measure multiscale FE (MFE) in continuous monitoring of REB health.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46406775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

TSCK guided parameter convex optimization tunable Q-factor wavelet transform and its application in wheelset bearing fault diagnosis TSCK引导参数凸优化可调q因子小波变换及其在轮对轴承故障诊断中的应用

IF 6.6 2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-04-17 DOI: 10.1177/14759217231167094

Xiong Zhang, Wenbo Wu, Jialu Li, Shuting Wan

{"title":"TSCK guided parameter convex optimization tunable Q-factor wavelet transform and its application in wheelset bearing fault diagnosis","authors":"Xiong Zhang, Wenbo Wu, Jialu Li, Shuting Wan","doi":"10.1177/14759217231167094","DOIUrl":"https://doi.org/10.1177/14759217231167094","url":null,"abstract":"Wheelset bearing is a typical vulnerable structural component in high-speed trains and heavy haul vehicles. In addition to the typical nonlinear and nonstationary characteristics, the vibration signal of wheelset bearing also contains track subgrade vibration and transmission path coupling interference components. To solve this problem, this paper proposes a new feature extraction method for wheelset bearing faults. This method constructs the Teager energy spectrum correlation kurtosis, which is purposely sensitive to periodic fault impulse components, as the objective function. The Q-factor and redundancy of tunable Q-factor wavelet transform are selected by using the parameter convex optimization method, which makes the signal decomposition have better sparsity, so as to extract fault information accurately. Simulated analysis, experimental signal analysis of QPZZ-II test-bed, and experimental signal analysis of wheelset bearing test-bed show that the proposed method can suppress the influence of nonperiodic transient impulse components, harmonic components, and noise components in the signal and accurately extract the periodic impact characteristics of bearings.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42180724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Flexible, monolithic piezoelectric sensors for large-area structural impact monitoring via MUSIC-assisted machine learning 通过音乐辅助机器学习，用于大面积结构冲击监测的柔性单片压电传感器

IF 6.6 2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-04-17 DOI: 10.1177/14759217231161812

Chen Zhu, Zhangyu Xu, Chao Hou, Xiaodong Lv, Shan Jiang, D. Ye, Y. Huang

{"title":"Flexible, monolithic piezoelectric sensors for large-area structural impact monitoring via MUSIC-assisted machine learning","authors":"Chen Zhu, Zhangyu Xu, Chao Hou, Xiaodong Lv, Shan Jiang, D. Ye, Y. Huang","doi":"10.1177/14759217231161812","DOIUrl":"https://doi.org/10.1177/14759217231161812","url":null,"abstract":"Aircraft smart skin requires the integration of large-scale, ultrathin, and high-sensitive sensor network on the surface for structural health monitoring (SHM). However, it is fairly difficult to fabricate such large-area flexible sensor networks with low cost and high reliability. Although flexible and monolithic sensors are easy to be fabricated, their applications in large-area impact monitoring have yet to be developed and revealed. In this study, an impact monitoring and localization technology based on monolithic small-area sensors is proposed for large-area structures. The pivotal principle lies in the combination of the flexible piezoelectric sensor array and the Multiple Signal Classification (MUSIC)-assisted Artificial Intelligence Network (ANN) algorithm, where the key sorted feature matrix from the output signals to ANN can be captured by the MUSIC algorithm to achieve the spatial location estimation. The results show that the flexible piezoelectric sensors demonstrate excellent performance to monitor structural impacts in a region of more than 7500% of its area. Secondly, excellent conformation between the sensors and complex surfaces is achieved by the ultrathin thickness almost without affecting the surficial flow field. The overall recognition accuracy and robustness of impact location of machine learning is greatly increased by the integration of MUSIC algorithm, which is immune to the shape, material, thickness, or opening holes of the structure. Except for the impact location, impact energy, impact frequency, impact hardness and other structural health parameters can also be monitored. Therefore, a great prospect in the integrated monitoring of the aircraft’s structural and environmental parameters is expected.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"83 8","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41304466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning enables nonlinear Lamb waves for precise location of fatigue crack 深度学习使非线性兰姆波能够精确定位疲劳裂纹

IF 6.6 2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-04-17 DOI: 10.1177/14759217231167076

Haiming Xu, Lishuai Liu, Jichao Xu, Y. Xiang, F. Xuan

{"title":"Deep learning enables nonlinear Lamb waves for precise location of fatigue crack","authors":"Haiming Xu, Lishuai Liu, Jichao Xu, Y. Xiang, F. Xuan","doi":"10.1177/14759217231167076","DOIUrl":"https://doi.org/10.1177/14759217231167076","url":null,"abstract":"Localization of fatigue cracks imposes immense significance to ensure the health of the engineering structures and prevent further catastrophic accidents. The nonlinear ultrasonic waves, especially the nonlinear Lamb waves, have been increasingly studied and employed for identifying micro-damages that are usually invisible to traditional linear ultrasonic waves. However, it remains a challenge to locate the fatigue cracks using nonlinear Lamb waves owing to the enormous difficulties in decoding location information from acoustic nonlinearity. Motivated by this, this work presents a data-driven method for precise location of fatigue crack using nonlinear Lamb waves. A 1D-Attention-convolutional neural network is developed to correlate the fatigue crack location with the wavelet coefficients at the second harmonic frequency of Lamb wave signals. The introduction of the Attention layer enables the models to pay more attention to the desired nonlinear features which dominates locating the fatigue crack. In particular, a convenient dataset creation scheme guided by the relative value label is proposed to generate sufficient data commonly required for deep learning approach. In addition, a lightweight single-excite-multiple-receive signal acquisition method is adopted instead of full-matrix capture method used in the traditional research, which highly improves detection efficiency. Numerical simulation and experimental validation manifest that the trained network can be used to establish the complex mapping between the nonlinear ultrasonic signals and the fatigue crack location features, so as to locate barely visible fatigue cracks. Our work provides a promising and practical way to facilitate nonlinear Lamb waves to accurately locate fatigue cracks in large-scale plate-like structures.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41615906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Long-term ultrasonic monitoring of concrete affected by alkali-silica reaction 碱-硅反应对混凝土影响的长期超声监测

IF 6.6 2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-04-15 DOI: 10.1177/14759217231169000

Hongbin Sun, Yalei Tang, C. Malone, Jinying Zhu

{"title":"Long-term ultrasonic monitoring of concrete affected by alkali-silica reaction","authors":"Hongbin Sun, Yalei Tang, C. Malone, Jinying Zhu","doi":"10.1177/14759217231169000","DOIUrl":"https://doi.org/10.1177/14759217231169000","url":null,"abstract":"This article presents continuous monitoring results of alkali-silica reaction (ASR) development in concrete specimens for over 400 days using ultrasonic testing and expansion measurements. Eight concrete specimens with nonreactive aggregate (Control), reactive coarse aggregate, and reactive fine aggregates were cast with two reinforced confinement conditions. The specimens were conditioned in an environmental chamber with high temperature and humidity (38°C and 90% relative humidity) to accelerate the ASR development. A multichannel ultrasonic monitoring system was developed to collect ultrasonic signals automatically, and the expansions in three directions were measured periodically. Results showed that the relative velocity change could detect the ASR initiation in all reactive specimens and show a correlation with expansion in the early stage. However, these correlations are inconsistent for different ASR specimens, and the velocity change becomes less sensitive to ASR damage in the late stage (after 300 days). Irrecoverable velocity drop was observed during every chamber shutdown period, especially in specimens with higher levels of ASR damage. This phenomenon suggests that the nonlinear ultrasonic response caused by the ambient temperature variation may indicate the ASR damage.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43224132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A novel method for early fault diagnosis of planetary gearbox with distributed tooth surface wear 一种具有分布齿面磨损的行星齿轮箱早期故障诊断方法

IF 6.6 2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-04-13 DOI: 10.1177/14759217231163887

Maosheng Gao, Z. Shang, Wanxiang Li, Fei Liu, Jingyu Liu

{"title":"A novel method for early fault diagnosis of planetary gearbox with distributed tooth surface wear","authors":"Maosheng Gao, Z. Shang, Wanxiang Li, Fei Liu, Jingyu Liu","doi":"10.1177/14759217231163887","DOIUrl":"https://doi.org/10.1177/14759217231163887","url":null,"abstract":"Planetary gearbox (PGB) usually work in harsh working conditions with low speed and heavy load, and they are prone to wear. Different from the local faults, the distributed faults such as tooth surface wear are often weak and difficult to detect in the early stage, and it is difficult to extract fault characteristic. This paper presents an early fault diagnosis method for the distributed tooth surface wear of PGB to solve this problem. The proposed multi-channel optimal maximum correlation kurtosis deconvolution (MCO_MCKD) algorithm is used to extract fault characteristic. In order to enhance the effect of fault characteristic extraction (FCE), the algorithm first uses the sliding window principle to segment the input signal to establishes multiple channels for maximum correlation kurtosis (max_CK) optimization based on all the short signals obtained. The finite impulse response (FIR) filter with the max_CK is selected to filter the input signal, in order to realize FCE. The influence of tooth wear is mainly reflected in the frequency-domain signal amplitude. In order to realize early fault diagnosis, the frequency-domain statistical indicator fault characteristic energy ratio (FCER) is proposed based on this characteristic. The health status of the equipment is monitored by calculating the FCER of the signal after FCE. Early fault diagnosis is realized based on the mutation of the FCER. The simulation results show that MCO_MCKD algorithm has strong robustness. The experimental results show this proposed method is effective and superior.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44870857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0