Pub Date : 2023-08-30DOI: 10.1177/14759217231191102
Shaohan Wang, Trenton Bryce Abbott, R. Fong, Cheryl Xu, F. Yuan
This paper explores a full-field non-contact optical sensing technique using a stereo camera for imaging hidden damage based on vibration-based damage detection methodology in structural health monitoring. The technique utilizes a pair of digital cameras to capture dynamic operational deflection shapes (ODSs) over the region of interest (ROI) of a structure’s surface via digital image correlation (DIC) when subjected to vibrational excitation. This research overcomes bottlenecks in using high vibration modes for imaging the hidden damage area by (1) applying DIC to operational modal analysis with simple pick-peaking techniques to gather natural frequencies and operational mode shapes in plate structures, while (2) using wavelet analysis to reveal the image of the damage region as a means for baseline-free global damage quantification. In the feasibility study, four cases with two aluminum plates with large damage regions were investigated with a vibration shaker generating a frequency sweep up to 1 kHz. The stereo camera imaged the speckled surface of the plate with white light. Once the dynamic ODSs in the ROI were observed using DIC, the natural frequencies and associated operational mode shapes were extracted using a peak-picking technique in the frequency spectrum. Natural frequencies and operational mode shapes from finite element analysis correlated well with the experimental observations from three-dimensional DIC for all 12 vibration modes respectively. A wavelet transform mode shape curvature (WT-MSC) technique to obtain the modal shape curvature via a two-dimensional continuous wavelet transform with a Mexican Hat analyzing wavelet was then implemented on each of the first 12 vibration mode shapes. A damage image condition that incorporates all weighted wavelet coefficients is proposed to image the damage region. The hidden damage was visualized clearly with WT-MSC, as the technique is much less sensitive to noise than the use of MSC alone, and the use of high vibration modes exhibiting larger mode shape curvatures provided a greater sensitivity for imaging the damage region. Hidden damage regions were successfully visualized in all four cases.
{"title":"Vibration-based hidden damage imaging using stereo cameras with digital image correlation","authors":"Shaohan Wang, Trenton Bryce Abbott, R. Fong, Cheryl Xu, F. Yuan","doi":"10.1177/14759217231191102","DOIUrl":"https://doi.org/10.1177/14759217231191102","url":null,"abstract":"This paper explores a full-field non-contact optical sensing technique using a stereo camera for imaging hidden damage based on vibration-based damage detection methodology in structural health monitoring. The technique utilizes a pair of digital cameras to capture dynamic operational deflection shapes (ODSs) over the region of interest (ROI) of a structure’s surface via digital image correlation (DIC) when subjected to vibrational excitation. This research overcomes bottlenecks in using high vibration modes for imaging the hidden damage area by (1) applying DIC to operational modal analysis with simple pick-peaking techniques to gather natural frequencies and operational mode shapes in plate structures, while (2) using wavelet analysis to reveal the image of the damage region as a means for baseline-free global damage quantification. In the feasibility study, four cases with two aluminum plates with large damage regions were investigated with a vibration shaker generating a frequency sweep up to 1 kHz. The stereo camera imaged the speckled surface of the plate with white light. Once the dynamic ODSs in the ROI were observed using DIC, the natural frequencies and associated operational mode shapes were extracted using a peak-picking technique in the frequency spectrum. Natural frequencies and operational mode shapes from finite element analysis correlated well with the experimental observations from three-dimensional DIC for all 12 vibration modes respectively. A wavelet transform mode shape curvature (WT-MSC) technique to obtain the modal shape curvature via a two-dimensional continuous wavelet transform with a Mexican Hat analyzing wavelet was then implemented on each of the first 12 vibration mode shapes. A damage image condition that incorporates all weighted wavelet coefficients is proposed to image the damage region. The hidden damage was visualized clearly with WT-MSC, as the technique is much less sensitive to noise than the use of MSC alone, and the use of high vibration modes exhibiting larger mode shape curvatures provided a greater sensitivity for imaging the damage region. Hidden damage regions were successfully visualized in all four cases.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44448241","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 : 2023-08-29DOI: 10.1177/14759217231191080
Sun-Ho Lee, Choon-su Park, D. Yoon
The failure of buried pipelines can lead to serious consequences such as explosions, environmental pollution, settlement, as well as economic loss. To prevent these outcomes, it is crucial to identify the causes of failure and monitor their signs. One of the main causes of failure is unexpected third-party interference (TPI), which is particularly challenging to detect. Regarding to this issue, this study proposes a new algorithm for monitoring impact damage, which can be used for prompt response and damage prevention. The algorithm is integrated into the system using two approaches. The first approach focuses on detecting the location of the damage (referred to as source location). A kurtosis-based transfer function was newly proposed to selecting the optimal frequency band for time-difference-of-arrival based source location, resulting in accurate pinpointing of damage, even in a noisy environment. The second approach is used to determine whether impact damage has actually occurred by observing newly suggested features in both the time and frequency domains (referred to as anomaly detection). These features evaluate the presence of damage and the similarity between signals. As a result, it was evaluated that the field applicability was higher than that of conventional methods, and the superiority of the proposed method was also verified through field experiments. The method proposed in this study is expected to enable immediate response when the integrity of the buried pipelines is on the line of failure due to TPI.
{"title":"Source location and anomaly detection for damage identification of buried pipelines using kurtosis-based transfer function","authors":"Sun-Ho Lee, Choon-su Park, D. Yoon","doi":"10.1177/14759217231191080","DOIUrl":"https://doi.org/10.1177/14759217231191080","url":null,"abstract":"The failure of buried pipelines can lead to serious consequences such as explosions, environmental pollution, settlement, as well as economic loss. To prevent these outcomes, it is crucial to identify the causes of failure and monitor their signs. One of the main causes of failure is unexpected third-party interference (TPI), which is particularly challenging to detect. Regarding to this issue, this study proposes a new algorithm for monitoring impact damage, which can be used for prompt response and damage prevention. The algorithm is integrated into the system using two approaches. The first approach focuses on detecting the location of the damage (referred to as source location). A kurtosis-based transfer function was newly proposed to selecting the optimal frequency band for time-difference-of-arrival based source location, resulting in accurate pinpointing of damage, even in a noisy environment. The second approach is used to determine whether impact damage has actually occurred by observing newly suggested features in both the time and frequency domains (referred to as anomaly detection). These features evaluate the presence of damage and the similarity between signals. As a result, it was evaluated that the field applicability was higher than that of conventional methods, and the superiority of the proposed method was also verified through field experiments. The method proposed in this study is expected to enable immediate response when the integrity of the buried pipelines is on the line of failure due to TPI.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48339623","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 : 2023-08-29DOI: 10.1177/14759217231190543
Xudong Jian, Ye Xia, E. Chatzi, Zhilu Lai
The identification of influence surfaces (ISs) for bridge structures offers an efficient tool for understanding traffic loads and assessing structural conditions. In general, ISs of a real bridge can be identified through calibration tests using calibration vehicles with known weights moving across the bridge. However, the existing methods face difficulties in considering comprehensive factors, such as the lateral movement, speed variation, and track width of the calibration vehicle, as well as bridge dynamic effects. These factors inevitably introduce inaccuracies into the task of identification. To comprehensively consider these factors, this study proposes a deep learning-based method that combines deep multilayer perceptrons (MLPs) and computer vision (CV), with deep MLP adopted to identify bridge ISs and CV employed to obtain the position coordinates of the calibration vehicle’s wheels. A series of numerical simulations and field experiments on an in-service bridge were carried out to validate the proposed framework and compare it against a broadly established method to such an end—Quilligan’s method. The results show the accuracy, robustness, and practicability of the proposed framework.
{"title":"Bridge influence surface identification using a deep multilayer perceptron and computer vision techniques","authors":"Xudong Jian, Ye Xia, E. Chatzi, Zhilu Lai","doi":"10.1177/14759217231190543","DOIUrl":"https://doi.org/10.1177/14759217231190543","url":null,"abstract":"The identification of influence surfaces (ISs) for bridge structures offers an efficient tool for understanding traffic loads and assessing structural conditions. In general, ISs of a real bridge can be identified through calibration tests using calibration vehicles with known weights moving across the bridge. However, the existing methods face difficulties in considering comprehensive factors, such as the lateral movement, speed variation, and track width of the calibration vehicle, as well as bridge dynamic effects. These factors inevitably introduce inaccuracies into the task of identification. To comprehensively consider these factors, this study proposes a deep learning-based method that combines deep multilayer perceptrons (MLPs) and computer vision (CV), with deep MLP adopted to identify bridge ISs and CV employed to obtain the position coordinates of the calibration vehicle’s wheels. A series of numerical simulations and field experiments on an in-service bridge were carried out to validate the proposed framework and compare it against a broadly established method to such an end—Quilligan’s method. The results show the accuracy, robustness, and practicability of the proposed framework.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45253896","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 : 2023-08-22DOI: 10.1177/14759217231193102
Lucas H. G. Resende, R. Finotti, F. Barbosa, Hernán Garrido, A. Cury, Martín Domizio
This work investigates the effectiveness of using convolutional neural networks (CNNs) and instantaneous displacement measurements for damage identification in beams. The study involves subjecting laboratory beams to eight distinct damage scenarios and capturing the vertical positions of 60 points along the beam length during free-vibration tests using a high-speed camera. The data obtained was subsequently used to train a CNN in a supervised manner to estimate the level of damage at each point. Results showed that the CNN models were able to correctly localize and quantify the damage levels when trained on data from all damage scenarios. The soundness of the proposed methodology was demonstrated in a robustness assessment, where all eight damage scenarios were correctly identified even when two of them were excluded from the training dataset.
{"title":"Damage identification using convolutional neural networks from instantaneous displacement measurements via image processing","authors":"Lucas H. G. Resende, R. Finotti, F. Barbosa, Hernán Garrido, A. Cury, Martín Domizio","doi":"10.1177/14759217231193102","DOIUrl":"https://doi.org/10.1177/14759217231193102","url":null,"abstract":"This work investigates the effectiveness of using convolutional neural networks (CNNs) and instantaneous displacement measurements for damage identification in beams. The study involves subjecting laboratory beams to eight distinct damage scenarios and capturing the vertical positions of 60 points along the beam length during free-vibration tests using a high-speed camera. The data obtained was subsequently used to train a CNN in a supervised manner to estimate the level of damage at each point. Results showed that the CNN models were able to correctly localize and quantify the damage levels when trained on data from all damage scenarios. The soundness of the proposed methodology was demonstrated in a robustness assessment, where all eight damage scenarios were correctly identified even when two of them were excluded from the training dataset.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45078121","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 : 2023-08-10DOI: 10.1177/14759217231191267
Qiao Bao, Tian Xie, Weiwei Hu, Kai Tao, Qiang Wang
Guided waves-based structural health monitoring (SHM) methods have potential for practical applications, since they are sensitive to small damages and are able to realize large area monitoring. Among these methods, the Reconstruction Algorithm for Probabilistic Inspection (RAPID), using a Piezoelectric transducer (PZT) sensor array, is one of the most widely used imaging algorithms to perform active damage monitoring and localization. However, since the sensing paths are distributed inside the sensor array with the non-uniform density, the RAPID algorithm can only localize damage when it is occurring inside of the array. If the damage occurs outside of the array or both inside and outside of the array, that is, multi-type damage, the performance of RAPID algorithm would not be satisfactory. In this paper, a scattering coefficient-based RAPID algorithm with damage indexes separation and imaging fusion is proposed. The amplitude of damage scattered signal at the corresponding time of fight is adopted as the weight in the probability distribution function, and damage indexes are then classified into two categories in the RAPID algorithm for the inside and outside damage localization respectively. Finally, an experiment on the complex composite plate, with the center large hole and surrounding bolt holes, is carried out to verify this proposed method. Experimental results show that this method can realize multi-type damage localization with errors less than 40 mm.
{"title":"Multi-type damage localization using the scattering coefficient-based RAPID algorithm with damage indexes separation and imaging fusion","authors":"Qiao Bao, Tian Xie, Weiwei Hu, Kai Tao, Qiang Wang","doi":"10.1177/14759217231191267","DOIUrl":"https://doi.org/10.1177/14759217231191267","url":null,"abstract":"Guided waves-based structural health monitoring (SHM) methods have potential for practical applications, since they are sensitive to small damages and are able to realize large area monitoring. Among these methods, the Reconstruction Algorithm for Probabilistic Inspection (RAPID), using a Piezoelectric transducer (PZT) sensor array, is one of the most widely used imaging algorithms to perform active damage monitoring and localization. However, since the sensing paths are distributed inside the sensor array with the non-uniform density, the RAPID algorithm can only localize damage when it is occurring inside of the array. If the damage occurs outside of the array or both inside and outside of the array, that is, multi-type damage, the performance of RAPID algorithm would not be satisfactory. In this paper, a scattering coefficient-based RAPID algorithm with damage indexes separation and imaging fusion is proposed. The amplitude of damage scattered signal at the corresponding time of fight is adopted as the weight in the probability distribution function, and damage indexes are then classified into two categories in the RAPID algorithm for the inside and outside damage localization respectively. Finally, an experiment on the complex composite plate, with the center large hole and surrounding bolt holes, is carried out to verify this proposed method. Experimental results show that this method can realize multi-type damage localization with errors less than 40 mm.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46085338","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}
Aiming at the problem of low accuracy of unsupervised learning anomaly detection algorithm, a dual-input anomaly detection method based on deep reinforcement learning was proposed. The proposed model mainly consists of a feature extractor and anomaly detector. Based on the deep reinforcement learning framework, the feature extractor uses a dual-input deep neural network to form the current value network and the target value network, which are used to extract the low-dimensional feature vectors. Based on the 3 σ principle, the reward function of reinforcement learning is designed to reward and punish the output results of the model during training. The model was trained only with the normal data, and the extracted feature vector of the normal class was used as the input of the anomaly detector to complete the learning of the detector. During the test, the input anomaly detection was realized based on the dual-input convolutional neural network, and the anomaly detector was completed by learning. To illustrate the generality and generalization performance of the proposed method, four sets of image data and two sets of rolling bearing fault data in different fields were verified respectively. At the same time, the proposed method is applied to the fault detection of a real aero-engine rolling bearing.The results show that the proposed model has high anomaly detection accuracy, which is superior to the current optimal method.
{"title":"Dual-input anomaly detection method based on deep reinforcement learning","authors":"Yuxiang Kang, Guo Chen, Hao Wang, Wenping Pan, Xunkai Wei","doi":"10.1177/14759217231188002","DOIUrl":"https://doi.org/10.1177/14759217231188002","url":null,"abstract":"Aiming at the problem of low accuracy of unsupervised learning anomaly detection algorithm, a dual-input anomaly detection method based on deep reinforcement learning was proposed. The proposed model mainly consists of a feature extractor and anomaly detector. Based on the deep reinforcement learning framework, the feature extractor uses a dual-input deep neural network to form the current value network and the target value network, which are used to extract the low-dimensional feature vectors. Based on the 3 σ principle, the reward function of reinforcement learning is designed to reward and punish the output results of the model during training. The model was trained only with the normal data, and the extracted feature vector of the normal class was used as the input of the anomaly detector to complete the learning of the detector. During the test, the input anomaly detection was realized based on the dual-input convolutional neural network, and the anomaly detector was completed by learning. To illustrate the generality and generalization performance of the proposed method, four sets of image data and two sets of rolling bearing fault data in different fields were verified respectively. At the same time, the proposed method is applied to the fault detection of a real aero-engine rolling bearing.The results show that the proposed model has high anomaly detection accuracy, which is superior to the current optimal method.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"1 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65887322","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 : 2023-08-08DOI: 10.1177/14759217231188184
Tengyu Ma, Q. Feng, Zhisen Tan, Jinping Ou
Distributed optical fiber acoustic sensing (DAS) technique has been applied in pipeline health monitoring, and the commonly used sensor is phase-sensitive optical time domain reflectometry. Most DAS monitoring systems can localize leakages of a pipeline but fail to identify potential non-destructive damages like bolt looseness on joints before the leakage occurs. An early damage identification is indispensable to averting severe leakages and secondary disasters. In this study, an optical phase mode analysis method is proposed for identifying pipeline bolt looseness. This method combines structure mode analysis and distributed optical phase demodulation to extract damage-related phase mode parameters. Two algorithms are specially designed for denoising and selecting signals essential for mode analysis. Phase time histories are retrieved from the original optical phase, which are decomposed to acquire phase mode shapes that can localize bolt looseness through Hilbert-Huang transform enhanced with bandwidth restricted empirical mode decomposition. Phase damping ratio is proposed to further quantify the looseness degree. Polarization diversity technique is employed to avoid polarization fading. An experiment was conducted upon a 3.2 m steel pipeline with flange joints. Bolt looseness on three joints are respectively localized even if only one bolt is loosened, obtaining a localization error of 0.07 m and 85.7% recognition ratio. The phase damping ratio shows apparent positive correlation with the number of loose bolts. The error of quantified loose bolt number is 0.79. The present study demonstrates how to localize and quantify pipeline bolt looseness through dynamical mode analysis for distributed optical phase. The developed method can identify potential damages that change the mechanical properties of a pipeline before they get severe, and holds promise in the long-distance health monitoring of other structures.
{"title":"Optical phase mode analysis method for pipeline bolt looseness identification using distributed optical fiber acoustic sensing","authors":"Tengyu Ma, Q. Feng, Zhisen Tan, Jinping Ou","doi":"10.1177/14759217231188184","DOIUrl":"https://doi.org/10.1177/14759217231188184","url":null,"abstract":"Distributed optical fiber acoustic sensing (DAS) technique has been applied in pipeline health monitoring, and the commonly used sensor is phase-sensitive optical time domain reflectometry. Most DAS monitoring systems can localize leakages of a pipeline but fail to identify potential non-destructive damages like bolt looseness on joints before the leakage occurs. An early damage identification is indispensable to averting severe leakages and secondary disasters. In this study, an optical phase mode analysis method is proposed for identifying pipeline bolt looseness. This method combines structure mode analysis and distributed optical phase demodulation to extract damage-related phase mode parameters. Two algorithms are specially designed for denoising and selecting signals essential for mode analysis. Phase time histories are retrieved from the original optical phase, which are decomposed to acquire phase mode shapes that can localize bolt looseness through Hilbert-Huang transform enhanced with bandwidth restricted empirical mode decomposition. Phase damping ratio is proposed to further quantify the looseness degree. Polarization diversity technique is employed to avoid polarization fading. An experiment was conducted upon a 3.2 m steel pipeline with flange joints. Bolt looseness on three joints are respectively localized even if only one bolt is loosened, obtaining a localization error of 0.07 m and 85.7% recognition ratio. The phase damping ratio shows apparent positive correlation with the number of loose bolts. The error of quantified loose bolt number is 0.79. The present study demonstrates how to localize and quantify pipeline bolt looseness through dynamical mode analysis for distributed optical phase. The developed method can identify potential damages that change the mechanical properties of a pipeline before they get severe, and holds promise in the long-distance health monitoring of other structures.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47581652","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 : 2023-08-08DOI: 10.1177/14759217231183142
Siyuan Sun, Bin Yang, Qilin Zhang, R. Wüchner, Licheng Pan, Haitao Zhu
Modal tracking plays a vital role in structural health monitoring since changes in modal parameters help us understand a structure’s dynamic characteristics and may reflect the potential deterioration of structural performance. Although numerous modal parameter estimation (MPE) methods exist, it is not guaranteed that an MPE process will exclude all spurious modes and not lose any physical modes every time over a long-term monitoring period. Relatively large damping of a structure, poor data quality, and significant changes in structural modal parameters may make the estimated modal parameters spurious, missing, or misclassified. It makes long-term modal tracking semiautomated or manual, which constrains timely downstream applications such as anomaly detection, condition assessment, and decision making. This research aims to propose a long-term continuous automatic modal tracking algorithm based on Bayesian inference even when the modal parameters, damping, and data quality change significantly. Bayesian inference is used to determine the physical modes from the results of existing MPE methods. Both the modes identified from the most recent response set and the modal probability model from multiple previous response sets are considered in the Bayesian model to better determine the physical modes from the results of MPE. Moreover, the proposed algorithm requires only three extra hyperparameters compared to general modal tracking algorithms, and they can be quickly determined by a grid search method. The performance of the proposed algorithm is verified by a numerical example and a real-world civil structure Z24 Bridge benchmark.
{"title":"Long-term continuous automatic modal tracking algorithm based on Bayesian inference","authors":"Siyuan Sun, Bin Yang, Qilin Zhang, R. Wüchner, Licheng Pan, Haitao Zhu","doi":"10.1177/14759217231183142","DOIUrl":"https://doi.org/10.1177/14759217231183142","url":null,"abstract":"Modal tracking plays a vital role in structural health monitoring since changes in modal parameters help us understand a structure’s dynamic characteristics and may reflect the potential deterioration of structural performance. Although numerous modal parameter estimation (MPE) methods exist, it is not guaranteed that an MPE process will exclude all spurious modes and not lose any physical modes every time over a long-term monitoring period. Relatively large damping of a structure, poor data quality, and significant changes in structural modal parameters may make the estimated modal parameters spurious, missing, or misclassified. It makes long-term modal tracking semiautomated or manual, which constrains timely downstream applications such as anomaly detection, condition assessment, and decision making. This research aims to propose a long-term continuous automatic modal tracking algorithm based on Bayesian inference even when the modal parameters, damping, and data quality change significantly. Bayesian inference is used to determine the physical modes from the results of existing MPE methods. Both the modes identified from the most recent response set and the modal probability model from multiple previous response sets are considered in the Bayesian model to better determine the physical modes from the results of MPE. Moreover, the proposed algorithm requires only three extra hyperparameters compared to general modal tracking algorithms, and they can be quickly determined by a grid search method. The performance of the proposed algorithm is verified by a numerical example and a real-world civil structure Z24 Bridge benchmark.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45513812","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 : 2023-08-07DOI: 10.1177/14759217231184589
Hanadi Mortada, Sarah El Mousharrafie, Elien Mahfoud, M. Harb
Composite materials are widely used in most industries due to their high specific strength, specific stiffness, and their relatively lighter weight compared to other traditional materials. However, the presence of defects arising from manufacturing processes or during service loads can make these structures more susceptible to a diminished performance. Furthermore, the former defects are inevitable in composite structures, but they can be reduced. Each type of defect requires specific inspection techniques and configurations. In this work, a review of the different types of composites manufacturing processes and their corresponding resultant defects is presented with the various nondestructive evaluation techniques employed for these defects’ characterization. The emphasis of this paper is on ultrasonic inspection and detection techniques for they present high sensitivity to surface/subsurface discontinuities, superior depth of ultrasonic penetration for flaw detection, feasibility on large scales, and instantaneous and detailed images production. Notably, noncontact ultrasonic testing techniques are also reviewed, air-coupled techniques in specific, and highlighted as a fine alternative to conventional contact inspection systems as they reduce the restrictions that coexist with the use of couplants. Moreover, these ultrasonic testing techniques are summarized to show the latest research progress achieved in the field of air-coupled ultrasonic inspection systems for manufacturing defects’ monitoring in composite structures including delamination, porosity, dryness, waviness, and resin lack/excess. Finally, we highlight the type and central frequency of the transducers and experimental results present in literature and obtained in terms of both detection and size of the defects.
{"title":"Noncontact nondestructive ultrasonic techniques for manufacturing defects monitoring in composites: a review","authors":"Hanadi Mortada, Sarah El Mousharrafie, Elien Mahfoud, M. Harb","doi":"10.1177/14759217231184589","DOIUrl":"https://doi.org/10.1177/14759217231184589","url":null,"abstract":"Composite materials are widely used in most industries due to their high specific strength, specific stiffness, and their relatively lighter weight compared to other traditional materials. However, the presence of defects arising from manufacturing processes or during service loads can make these structures more susceptible to a diminished performance. Furthermore, the former defects are inevitable in composite structures, but they can be reduced. Each type of defect requires specific inspection techniques and configurations. In this work, a review of the different types of composites manufacturing processes and their corresponding resultant defects is presented with the various nondestructive evaluation techniques employed for these defects’ characterization. The emphasis of this paper is on ultrasonic inspection and detection techniques for they present high sensitivity to surface/subsurface discontinuities, superior depth of ultrasonic penetration for flaw detection, feasibility on large scales, and instantaneous and detailed images production. Notably, noncontact ultrasonic testing techniques are also reviewed, air-coupled techniques in specific, and highlighted as a fine alternative to conventional contact inspection systems as they reduce the restrictions that coexist with the use of couplants. Moreover, these ultrasonic testing techniques are summarized to show the latest research progress achieved in the field of air-coupled ultrasonic inspection systems for manufacturing defects’ monitoring in composite structures including delamination, porosity, dryness, waviness, and resin lack/excess. Finally, we highlight the type and central frequency of the transducers and experimental results present in literature and obtained in terms of both detection and size of the defects.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45934205","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 : 2023-08-07DOI: 10.1177/14759217231190041
Laurence Clarkson, Yifeng Zhang, F. Cegla
Failure of pipe network components in so-called mixing zones due to high-cycle thermal fatigue (HCTF) can occur within nuclear power plants where fluids of different thermal and hydraulic properties interact. Given that the consequences of such failures are potentially deadly, a method to monitor HCTF non-invasively in real-time is expected to be of great use. This method may be realised by a technique to determine the inaccessible temperature distribution of a component since thermal gradients drive HCTF. Previous work showed that a physics-based method called the inverse thermal modelling (ITM) method can obtain the temperature distribution from external temperature and ultrasonic time of flight (TOF) measurements. This study investigated whether the long-short-term memory (LSTM) machine learning architecture could be a faster alternative to the ITM method for data inversion. On experimental data, a 25-member ensemble of LSTM networks achieved an ensemble median root mean square error (RMSE) of 1.04°C and an ensemble median mean error of 0.194°C (both relative to a resistance temperature device measurement). These values are similar to the ITM method which achieved a RMSE of 1.04°C and a mean error of 0.196°C. The single LSTM network and the ITM method achieved a computation-to-real-world time ratio of 0.008% and 14%, respectively demonstrating that both methods can invert data in real-time. Simulation studies revealed that LSTM performance is sensitive to small differences between the training and real-world parameters leading to unacceptable errors. However, these errors can be detected via an ensemble of independent networks and, corrected by simply adding a correction factor to the TOF prior to being input into the networks. The results show that LSTM has the potential to be an alternative to the ITM method; however, the authors favour ITM for temperature distribution monitoring given its interpretability.
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