Structural Health Monitoring-An International Journal最新文献_第2页

RFIS-HI: a new health indicator for quantitative condition monitoring of the bearing under variable speed conditions rfisi - hi:一种用于变速条件下轴承定量状态监测的新型健康指标

2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-11-07 DOI: 10.1177/14759217231203244

Weipeng Ma, Yaoxiang Yu, Liang Guo, Mengui Qian, Hongli Gao

{"title":"RFIS-HI: a new health indicator for quantitative condition monitoring of the bearing under variable speed conditions","authors":"Weipeng Ma, Yaoxiang Yu, Liang Guo, Mengui Qian, Hongli Gao","doi":"10.1177/14759217231203244","DOIUrl":"https://doi.org/10.1177/14759217231203244","url":null,"abstract":"The health indicator (HI) plays a crucial role in the condition monitoring of the rolling bearing. However, most existing HIs exhibit significant fluctuations when the speed changes. To address the issue, this paper proposes a new HI namely reweighted fault impact strength (RFIS)-HI. First, sub-signals are obtained through a frequency division strategy, and corresponding resampled signals are derived using order tracking. Second, the average impact peak in the time domain is acquired to measure the impact of the signal. According to fault characteristic order (FCO), the ratio of FCOs summation to noise amplitude in the frequency domain is obtained to measure periodicity. Then, the FISgram is constructed for selecting the optimal frequency band. To better quantify the degradation degree of the bearing, different weights are assigned and optimized for constructing RFIS. Finally, the influence of rotational speed on RFIS is eliminated by utilizing prior knowledge. Taking the first 10% of the dataset as baseline data, RFIS-HI is constructed through relative similarity. In this paper, a bearing dataset under time-varying speed conditions and an XJTU-SY dataset are used for verification. Results show that the proposed HI can achieve better trendability, scale similarity, and stability.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135540104","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

Long-term creep monitoring of composite wing leading edge using embedded fiber Bragg grating 基于嵌入式光纤光栅的复合材料机翼前缘长期蠕变监测

2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-11-07 DOI: 10.1177/14759217231202919

Changhao Chen, Qi Wu, Zheng Zhang, Zhixiang Liu, Ke Xiong

引用次数: 0

A novel spatiotemporal 3D CNN framework with multi-task learning for efficient structural damage detection 基于多任务学习的新型时空三维CNN框架用于结构损伤检测

2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-11-06 DOI: 10.1177/14759217231206178

Sadeq Kord, Touraj Taghikhany, Mohammad Akbari

{"title":"A novel spatiotemporal 3D CNN framework with multi-task learning for efficient structural damage detection","authors":"Sadeq Kord, Touraj Taghikhany, Mohammad Akbari","doi":"10.1177/14759217231206178","DOIUrl":"https://doi.org/10.1177/14759217231206178","url":null,"abstract":"In recent years, convolutional neural networks (CNNs) have demonstrated promising results in detecting structural damage. However, their architectures often overlook spatial and temporal effects simultaneously. This limitation can result in the loss of valuable information and an incapability to fully capture the complexity of the data, ultimately leading to reduced accuracy and suboptimal performance. This study proposes an intuitive three-dimensional CNN architecture that takes into account vibration history along with sensor spatial relations based on their relative positions. Furthermore, a multi-task learning (MTL) approach is suggested, which is a powerful approach for performing multiple tasks with a single network. The proposed 3D CNN method has been employed to detect single and double damage cases in an experimental steel frame through conventional classification alongside the transfer learning (TL). Moreover, MTL is used to detect single and double damage scenarios with a single unified network, which evaluates damage presence in separate tasks. The 3D CNN fulfilled state-of-the-art performance and 100% accuracy in detecting structural damage in almost all experiments. Additionally, the MTL model achieved promising results even in the presence of severe imbalanced classes of data. Furthermore, it was observed that the utilization of TL resulted in a notable reduction of computation time by 68% and the number of trainable parameters by 90% with the same level of accuracy in double-damage cases.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135684072","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

Sleeved waveguide ultrasonic sensor for monitoring concrete health 用于监测混凝土健康状况的套管波导超声波传感器

2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-11-04 DOI: 10.1177/14759217231191964

Wilson M Kairu, Michael J Gatari, Siphila W Mumenya, Prabhu Rajagopal

{"title":"Sleeved waveguide ultrasonic sensor for monitoring concrete health","authors":"Wilson M Kairu, Michael J Gatari, Siphila W Mumenya, Prabhu Rajagopal","doi":"10.1177/14759217231191964","DOIUrl":"https://doi.org/10.1177/14759217231191964","url":null,"abstract":"This article reports the development of a novel embedded waveguide ultrasonic sensor for detecting the onset of damage in reinforced concrete structures. A sleeved waveguide is proposed to confine guided ultrasonic waves in one-dimension, with leakage to the surrounding media only through specially created openings, thus reducing attenuation losses and enhancing the capability to inspect large structures from a single transducer location. The test frequency and mode are identified through modelling, and the interaction of leaky guided ultrasonic waves with delamination within the concrete volume is studied. Numerical simulations validated by experiments are used to study the changes in wave features such as mode velocity, wavelength and wave reflection in the delamination region, helping to estimate its location. Further simulation studies are carried out to demonstrate the possibility of using multiple waveguide sensors and sleeve openings to provide a full view of the concrete volume. The results are encouraging for practical long-range and large-scale monitoring of concrete volumes using the proposed sleeved waveguide ultrasonic sensors.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135773534","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

Bridge anomaly detection based on reconstruction error and structural similarity of unsupervised convolutional auto-encoder 基于重构误差和结构相似度的无监督卷积自编码器桥异常检测

2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-11-04 DOI: 10.1177/14759217231200096

Shuai Teng, Zongchao Liu, Wenjun Luo, Gongfa Chen, Li Cheng

{"title":"Bridge anomaly detection based on reconstruction error and structural similarity of unsupervised convolutional auto-encoder","authors":"Shuai Teng, Zongchao Liu, Wenjun Luo, Gongfa Chen, Li Cheng","doi":"10.1177/14759217231200096","DOIUrl":"https://doi.org/10.1177/14759217231200096","url":null,"abstract":"This study presents a novel bridge anomaly detection approach that employs the reconstruction error and structural similarity of an unsupervised convolutional auto-encoder. The presence of structural damage in a bridge typically results in changes in its vibration signals, and thus, the use of these signals for structural damage detection (SDD) has been widely investigated, with many methods relying on supervised learning. However, such existing SDD methods based on the supervised learning require prior knowledge of the damage states and cannot process monitoring data in real-time, thereby limiting their application to in-service bridges. To address this challenge, the authors propose the use of a convolutional auto-encoder as the reconstruction algorithm for real-time vibration signals. The auto-encoder is trained using normal signals and then used to reconstruct new inputs (either normal or abnormal). Two damage indicators (reconstruction error and structural similarity) are then calculated based on the reconstruction results and clustered to detect abnormal signals. The proposed approach was applied to the detection of various abnormalities in the old ADA Bridge, the results were 100% accurate, and about a 10% increase in accuracy was observed when compared to other control experiments. These results demonstrate the effectiveness of the proposed approach, with the auto-encoder achieving excellent reconstruction results for normal signals and clear discrepancies for abnormal signals. The proposed method was also validated on a cable-stayed bridge and an arch bridge, demonstrating its wide applicability in bridge anomaly detection.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135773690","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

Intelligent fault diagnosis of rotating machinery under variable working conditions based on deep transfer learning with fusion of local and global time–frequency features 基于局部与全局时频特征融合的深度迁移学习的变工况旋转机械故障智能诊断

2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-11-03 DOI: 10.1177/14759217231199427

Xiao Yu, Songcheng Wang, Hongyang Xu, Kun Yu, Ke Feng, Yongchao Zhang, Xiaowen Liu

{"title":"Intelligent fault diagnosis of rotating machinery under variable working conditions based on deep transfer learning with fusion of local and global time–frequency features","authors":"Xiao Yu, Songcheng Wang, Hongyang Xu, Kun Yu, Ke Feng, Yongchao Zhang, Xiaowen Liu","doi":"10.1177/14759217231199427","DOIUrl":"https://doi.org/10.1177/14759217231199427","url":null,"abstract":"With the development of deep learning methods, the data-driven fault diagnosis methods have attracted a great deal of interest. However, as for the data-driven fault diagnosis methods, technology has to overcome various difficulties in the practical industrial scenarios, such as variable working conditions, insufficient effective samples, and environmental noise interference. Combining with the time–frequency analysis of vibration signals, a domain adaptation fault diagnosis model based on ResNet and Transformer (DAFDMRT) is proposed in this work, aiming to solve the problems encountered by current rotating machinery fault diagnosis methods in the field of application. Firstly, the vibration signal is processed by wavelet packet transform and the time–frequency information grayscale maps is constructed. Next, a deep fusion feature extraction network combining ResNet and Transformer encoder, is designed for the extraction and fusion of the local and global features of multi-scale time–frequency information. Finally, the multi-kernel maximum mean discrepancy is applied to measure and minimize the distribution difference between the deep features of source and target domain, thereby improving the diagnostic performance of the diagnosis model in variable working conditions. In this work, comparative experiments are conducted as for bearing and gearbox datasets under variable working conditions. The results indicate that DAFDMRT can show excellent performances in terms of fault diagnosis and generalization ability.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135819901","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

An enhanced topological analysis for Lamb waves based SHM methods 基于SHM方法的Lamb波拓扑分析

2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-10-21 DOI: 10.1177/14759217231196217

Arthur Lejeune, Nicolas Hascoët, Marc Rébillat, Eric Monteiro, Nazih Mechbal

{"title":"An enhanced topological analysis for Lamb waves based SHM methods","authors":"Arthur Lejeune, Nicolas Hascoët, Marc Rébillat, Eric Monteiro, Nazih Mechbal","doi":"10.1177/14759217231196217","DOIUrl":"https://doi.org/10.1177/14759217231196217","url":null,"abstract":"Topological data analysis (TDA) is a powerful and promising tool for data analysis, but yet not exploited enough. It is a multidimensional method which can extract the topological features contained in a given dataset. An original TDA-based method allowing to monitor the health of structures when equipped with piezoelectric transducers (PZTs) is introduced here. Using a Lamb wave based Structural Health Monitoring (SHM) approach, it is shown that with specific pre-processing of the measured time-series data, the TDA (persistent homology) for damage detection and classification can be greatly improved. The TDA tool is first applied directly in a traditional manner in order to use homology classes to assess damage. After that, another method based on slicing the temporal data is developed to improve the persistence homology perception and to leverage topological descriptors to discriminate different damages. The dataset used to apply both methods comes from experimental campaigns performed on aeronautical composite plates with embedded PZTs where different damage types have been investigated such as delamination, impacts and stiffness reduction. The proposed approach enables to consider a priori physical information and provides a better way to classify damages than the traditional TDA approach. In summary, this article demonstrates that manipulating the topological the features of PZTs time-series signals using TDA provides an efficient mean to separate and classify the damage natures and opens the way for further developments on the use of TDA in SHM.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135513284","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

All-fiber photoacoustic system for large-area nondestructive testing 用于大面积无损检测的全光纤光声系统

2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-10-12 DOI: 10.1177/14759217231202546

Yuliang Wu, Xuelei Fu, Jiapu Li, Pengyu Zhang, Honghai Wang, Zhengying Li

{"title":"All-fiber photoacoustic system for large-area nondestructive testing","authors":"Yuliang Wu, Xuelei Fu, Jiapu Li, Pengyu Zhang, Honghai Wang, Zhengying Li","doi":"10.1177/14759217231202546","DOIUrl":"https://doi.org/10.1177/14759217231202546","url":null,"abstract":"Nondestructive testing (NDT) is of paramount importance in ensuring the safe operation of equipment. Among various NDT techniques, ultrasonic NDT has garnered widespread attention due to its high sensitivity, fast speed, and accurate defect location. Photoacoustic NDT, a burgeoning field in ultrasonic NDT, is particularly attractive due to its immunity to electromagnetic interference. However, existing photoacoustic NDT systems suffer from inadequate excitation intensity and complex ultrasonic signal characteristics, impeding large-area NDT and accurate crack visualization. In this study, we present an all-fiber photoacoustic system for large-area NDT. To address the issues, we have developed a photoacoustic generator unit that can be optimized and controlled to generate stronger ultrasonic signals. Furthermore, we have employed mode decomposition to simplify the detected ultrasonic signals by mitigating the acoustic impedance mismatch-induced mode mixing problem in the system. As a result, the technology allows for large-area crack monitoring of up to 50*50 cm 2 with an improved resolution of 1 mm. The present technology paves the way for high-resolution equipment crack monitoring with substantially enhanced accuracy in various environments.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136012535","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

Fabrication, characterization, and repair of nanocarbon-loaded aircraft paint-based sensors for real-world SHM: studies at the laboratory scale 真实世界SHM中载纳米碳飞机涂料传感器的制造、表征和修复:实验室规模的研究

2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-10-12 DOI: 10.1177/14759217231198015

Carlos Cuellar, Kaitlyn Watson, Elisabeth Smela

{"title":"Fabrication, characterization, and repair of nanocarbon-loaded aircraft paint-based sensors for real-world SHM: studies at the laboratory scale","authors":"Carlos Cuellar, Kaitlyn Watson, Elisabeth Smela","doi":"10.1177/14759217231198015","DOIUrl":"https://doi.org/10.1177/14759217231198015","url":null,"abstract":"There has been considerable interest in piezoresistive nanocarbon-loaded polymer films for structural health monitoring, including damage detection and strain monitoring. While good performance has been demonstrated, issues related to practical implementation have received less attention. Here we present sensors made from exfoliated graphite nanoplatelets (xGnP) incorporated into a commercial paint that is applied to Sikorsky aircraft. A formulation and a fabrication method are developed that deliver high piezoresistive strain sensitivity alongside mechanical integrity. At approximately 7 wt% xGnP, the gauge factor in tension is in the range of 30–55, and the effectiveness of the sensors for damage monitoring is demonstrated by the detection of perforations. To obtain a paintable solution, key considerations in choosing the solvent employed for introducing the nanocarbon are compatibility and the ability to keep the nanocarbon suspended, which is achieved using ethyl acetate. The ability to form sensors in situ on aircraft structures requires an uncomplicated method of making robust electrical connections, which is demonstrated here using embedded copper mesh. The strong, often nonlinear, environmental sensitivity of polymer-nanocarbon materials must also be considered in applications; here, increasing temperature and humidity both raise sensor resistance. This work shows that a second, unstrained reference sensor would work well for automatic compensation. Lastly, a method for effecting a repair that employs standard processes and maintains the high gauge factor is demonstrated. With these advances, the paint-xGnP sensors are ready for in-the-field testing on aircraft.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136013538","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

Multiple faults separation and identification of rolling bearings based on time-frequency spectrogram 基于时频谱的滚动轴承多故障分离与识别

2区工程技术

Structural Health Monitoring-An International Journal Pub Date : 2023-10-10 DOI: 10.1177/14759217231197110

Ming Lv, Changfeng Yan, Jianxiong Kang, Jiadong Meng, Zonggang Wang, Shengqiang Li, Bin Liu

{"title":"Multiple faults separation and identification of rolling bearings based on time-frequency spectrogram","authors":"Ming Lv, Changfeng Yan, Jianxiong Kang, Jiadong Meng, Zonggang Wang, Shengqiang Li, Bin Liu","doi":"10.1177/14759217231197110","DOIUrl":"https://doi.org/10.1177/14759217231197110","url":null,"abstract":"Rolling bearings play a crucial role as components in rotating machinery across various industrial fields. Bearing faults can potentially lead to severe accidents in operating machines. Therefore, condition monitoring and fault diagnosis of rolling bearings are essential for preventing equipment failures. Multiple faults are a common occurrence resulting from the prolonged operation of rolling bearings, and numerous research efforts have been made to study multiple faults in different components of the bearing. However, diagnosing multiple faults in a single component of the rolling bearing still remains a highly challenging task. In this paper, a multiple faults separation and identification method based on time-frequency (TF) spectrogram (TFS) is proposed for vibration signals of rolling bearings. Firstly, the fast path optimization method is improved to match the TFS of original vibration signals in bearing faults generated by short-time Fourier transform. Then multiple TF curves are extracted from the TFS by the proposed multiple transient component curves extraction method based on the improved fast path optimization method. With the fault characteristic period, a classification criterion is introduced to separate TF curves. Secondly, a TF masking method is constructed to retain the TF information closely related to fault components of vibration signals. Finally, the novel TF representation can be obtained to develop signal reconstruction, and multiple faults can be detected based on envelope analysis separately. The experiments from rolling bearings with multiple faults on raceways are used to verify the effectiveness of the proposed methods.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136295115","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