Pub Date : 2023-11-07DOI: 10.1177/14759217231202919
Changhao Chen, Qi Wu, Zheng Zhang, Zhixiang Liu, Ke Xiong
The creep behavior of composite wing leading edges resulting from nonequilibrium residual stresses and material viscoelasticity needs to be evaluated comprehensively as it significantly affects assembly. In this study, long-term creep monitoring of a composite wing leading edge used in an actual airplane for 710 h is conducted using embedded fiber Bragg grating arrays and a creep extraction algorithm. The spectra and Bragg wavelength shifts of two embedded arrays, which involve temperature, thermal expansion, and creep, are recorded and analyzed. The creep curve of the composite wing leading edge is reconstructed and further predicted using the creep extraction algorithm based on multiparameter decoupling and the Burgers model. This study elucidates the enlargement of the opening size in the composite wing leading edge by measuring the tensile strain above the neutral axis. The predicted creep time serves as a valuable reference for determining the appropriate assembly timing.
{"title":"Long-term creep monitoring of composite wing leading edge using embedded fiber Bragg grating","authors":"Changhao Chen, Qi Wu, Zheng Zhang, Zhixiang Liu, Ke Xiong","doi":"10.1177/14759217231202919","DOIUrl":"https://doi.org/10.1177/14759217231202919","url":null,"abstract":"The creep behavior of composite wing leading edges resulting from nonequilibrium residual stresses and material viscoelasticity needs to be evaluated comprehensively as it significantly affects assembly. In this study, long-term creep monitoring of a composite wing leading edge used in an actual airplane for 710 h is conducted using embedded fiber Bragg grating arrays and a creep extraction algorithm. The spectra and Bragg wavelength shifts of two embedded arrays, which involve temperature, thermal expansion, and creep, are recorded and analyzed. The creep curve of the composite wing leading edge is reconstructed and further predicted using the creep extraction algorithm based on multiparameter decoupling and the Burgers model. This study elucidates the enlargement of the opening size in the composite wing leading edge by measuring the tensile strain above the neutral axis. The predicted creep time serves as a valuable reference for determining the appropriate assembly timing.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"45 191","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135540141","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}
Pub Date : 2023-11-06DOI: 10.1177/14759217231206178
Sadeq Kord, Touraj Taghikhany, Mohammad Akbari
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.
{"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":"13 1","pages":"0"},"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}
Pub Date : 2023-11-04DOI: 10.1177/14759217231191964
Wilson M Kairu, Michael J Gatari, Siphila W Mumenya, Prabhu Rajagopal
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.
{"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":"6 4","pages":"0"},"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}
Pub Date : 2023-11-04DOI: 10.1177/14759217231200096
Shuai Teng, Zongchao Liu, Wenjun Luo, Gongfa Chen, Li Cheng
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.
{"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":"8 6","pages":"0"},"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}
Pub Date : 2023-11-03DOI: 10.1177/14759217231199427
Xiao Yu, Songcheng Wang, Hongyang Xu, Kun Yu, Ke Feng, Yongchao Zhang, Xiaowen Liu
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.
{"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":"35 16","pages":"0"},"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}
Pub Date : 2023-10-21DOI: 10.1177/14759217231196217
Arthur Lejeune, Nicolas Hascoët, Marc Rébillat, Eric Monteiro, Nazih Mechbal
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.
{"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":"7 4","pages":"0"},"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}
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.
无损检测是保证设备安全运行的重要手段。在各种无损检测技术中，超声无损检测以其灵敏度高、速度快、缺陷定位准确等优点得到了广泛的关注。光声无损检测是超声无损检测中的一个新兴领域，由于其抗电磁干扰的特性而受到广泛的关注。然而，现有的光声无损检测系统存在激发强度不足、超声信号特征复杂等问题，阻碍了大面积无损检测和裂纹的精确显示。在这项研究中，我们提出了一种用于大面积无损检测的全光纤光声系统。为了解决这些问题，我们开发了一种可以优化和控制的光声发生器单元，以产生更强的超声波信号。此外，我们还利用模态分解来简化检测到的超声信号，以减轻系统中声阻抗不匹配引起的模态混叠问题。因此，该技术允许对高达50*50 cm 2的大面积裂缝进行监测，分辨率提高到1 mm。目前的技术为高分辨率设备裂缝监测铺平了道路，大大提高了各种环境下的精度。
{"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":"23 1","pages":"0"},"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}
Pub Date : 2023-10-12DOI: 10.1177/14759217231198015
Carlos Cuellar, Kaitlyn Watson, Elisabeth Smela
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.
{"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":"43 1","pages":"0"},"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}
Pub Date : 2023-10-10DOI: 10.1177/14759217231197110
Ming Lv, Changfeng Yan, Jianxiong Kang, Jiadong Meng, Zonggang Wang, Shengqiang Li, Bin Liu
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.
{"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":"4 1","pages":"0"},"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}
Pub Date : 2023-10-10DOI: 10.1177/14759217231196216
R. Vidya Sagar, Indrashish Saha, Dibya Jyoti Basu, Tribikram Kundu
This article reports on the characteristics of fracture process zone in steel fibre-reinforced concrete (SFRC) under the mode I fracture process using acoustic emission (AE) testing. The generated AE waveforms during mode I fracture process in SFRC were recorded in the laboratory. Using a statistical analysis of AE waveforms, it was observed that as the loading increases, a damage zone consisting of numerous microcracks develops ahead of the predefined notch tip. The location of the generated AE events related to the numerous microcracks were classified into three zones namely (i) major damage, (ii) moderate damage and (iii) low damage. The areas of these regions were evaluated from the distribution of the AE events around the pre-notch. The number of AE events reduced with the increase in the steel fibre content under the same experimental conditions. The major damage zone was located ahead of the notch tip very closely and it comprised of AE events with (i) high peak amplitude, (ii) low information entropy and (iii) longer AE waveform duration.
{"title":"Statistics of acoustic emission waveforms in characterizing the fracture process zone in fibre-reinforced cementitious materials under mode I fracture","authors":"R. Vidya Sagar, Indrashish Saha, Dibya Jyoti Basu, Tribikram Kundu","doi":"10.1177/14759217231196216","DOIUrl":"https://doi.org/10.1177/14759217231196216","url":null,"abstract":"This article reports on the characteristics of fracture process zone in steel fibre-reinforced concrete (SFRC) under the mode I fracture process using acoustic emission (AE) testing. The generated AE waveforms during mode I fracture process in SFRC were recorded in the laboratory. Using a statistical analysis of AE waveforms, it was observed that as the loading increases, a damage zone consisting of numerous microcracks develops ahead of the predefined notch tip. The location of the generated AE events related to the numerous microcracks were classified into three zones namely (i) major damage, (ii) moderate damage and (iii) low damage. The areas of these regions were evaluated from the distribution of the AE events around the pre-notch. The number of AE events reduced with the increase in the steel fibre content under the same experimental conditions. The major damage zone was located ahead of the notch tip very closely and it comprised of AE events with (i) high peak amplitude, (ii) low information entropy and (iii) longer AE waveform duration.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136295578","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：