Pub Date : 2023-07-08DOI: 10.1177/14759217231185051
Weihan Shao, Hu Sun, Qifeng Zhou, Yishou Wang, X. Qing
Lamb wave-based damage quantification in large-scale composites has always been one of the concerning and intractable problems in aircraft structural health monitoring. In recent years, machine learning (ML) algorithms have been utilized to deeply explore the damage feature of Lamb wave signals, which aims to enhance the precision and accuracy of damage quantification. However, multi-damage quantification becomes one of the bottleneck problems because ML algorithms critically depend on the dataset. In this paper, a prioritizing selection and orderly permutation method is proposed to construct multi-damage dataset based on Born approximation principle, which shows the interaction between wave signals under multi- and single-damage conditions. Based on the multi-damage dataset, a multi-task deep learning algorithm is introduced to identify multiple damage, including the damage number, location, and size, in composite laminates. In the algorithm, a multi-branch 1D-convolution neural network framework, which includes a trunk network and branch networks is established to explore the damage features in Lamb wave scattering signals. Compared with single-task models, it has the ability to learn shared features for multiple tasks, effectively boosting the task results. The results show that the proposed multi-task learning (MTL) method saves 23.03% training time compared with the single-task learning method. In the task of quantifying multiple damage of composite laminate, the results of MTL are good for both the constructed test set and the measured test set, especially in the quantification of damage size, which shows the feasibility and reliability of this method.
{"title":"A novel Lamb wave-based multi-damage dataset construction and quantification algorithm under the framework of multi-task deep learning","authors":"Weihan Shao, Hu Sun, Qifeng Zhou, Yishou Wang, X. Qing","doi":"10.1177/14759217231185051","DOIUrl":"https://doi.org/10.1177/14759217231185051","url":null,"abstract":"Lamb wave-based damage quantification in large-scale composites has always been one of the concerning and intractable problems in aircraft structural health monitoring. In recent years, machine learning (ML) algorithms have been utilized to deeply explore the damage feature of Lamb wave signals, which aims to enhance the precision and accuracy of damage quantification. However, multi-damage quantification becomes one of the bottleneck problems because ML algorithms critically depend on the dataset. In this paper, a prioritizing selection and orderly permutation method is proposed to construct multi-damage dataset based on Born approximation principle, which shows the interaction between wave signals under multi- and single-damage conditions. Based on the multi-damage dataset, a multi-task deep learning algorithm is introduced to identify multiple damage, including the damage number, location, and size, in composite laminates. In the algorithm, a multi-branch 1D-convolution neural network framework, which includes a trunk network and branch networks is established to explore the damage features in Lamb wave scattering signals. Compared with single-task models, it has the ability to learn shared features for multiple tasks, effectively boosting the task results. The results show that the proposed multi-task learning (MTL) method saves 23.03% training time compared with the single-task learning method. In the task of quantifying multiple damage of composite laminate, the results of MTL are good for both the constructed test set and the measured test set, especially in the quantification of damage size, which shows the feasibility and reliability of this method.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45401736","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 : 2023-07-08DOI: 10.1177/14759217231174894
Hewenxuan Li, D. Chelidze
This paper presents a new fatigue damage detection and monitoring approach using a geometry-informed implementation of phase space warping (PSW). The proposed method is based on continuous-time PSW theory and geometric constructs, which clarifies the relationship between the deformation of the reconstructed phase flow and the underlying damage evolution. A discrete-time approximation to the continuous-time theory is established for practical applications. The practical geometry-informed PSW (GIPSW) algorithm is developed with integrated geometry-informed heuristics and global sensitivity analysis to monitor fatigue damage evolution accurately. The proposed method is validated through numerical experiments simulating nonlinear systems with varying fatigue damage dynamics, exhibiting distinct response complexities. The results show that the GIPSW improves the monitoring accuracy by at least 41.4% and can achieve maximally four-orders-of-magnitude-lower monitoring error compared with the conventional PSW algorithm. The GIPSW is also applied in physical experiments exploring raster-angle-affected fatigue damage dynamics in 3D-printed materials. The estimated hidden-fatigue damage-time history reveals distinct crack propagation rates differentiated by the raster angles and can be used for damage prognosis and modeling the fatigue mechanisms. The critical inflection points identified in the incremental damage-time histories detect the crack growth phase transitions as early as 0.17 of the total time to failure, which can be used for early damage awareness.
{"title":"Geometry-informed phase space warping for reliable fatigue damage monitoring","authors":"Hewenxuan Li, D. Chelidze","doi":"10.1177/14759217231174894","DOIUrl":"https://doi.org/10.1177/14759217231174894","url":null,"abstract":"This paper presents a new fatigue damage detection and monitoring approach using a geometry-informed implementation of phase space warping (PSW). The proposed method is based on continuous-time PSW theory and geometric constructs, which clarifies the relationship between the deformation of the reconstructed phase flow and the underlying damage evolution. A discrete-time approximation to the continuous-time theory is established for practical applications. The practical geometry-informed PSW (GIPSW) algorithm is developed with integrated geometry-informed heuristics and global sensitivity analysis to monitor fatigue damage evolution accurately. The proposed method is validated through numerical experiments simulating nonlinear systems with varying fatigue damage dynamics, exhibiting distinct response complexities. The results show that the GIPSW improves the monitoring accuracy by at least 41.4% and can achieve maximally four-orders-of-magnitude-lower monitoring error compared with the conventional PSW algorithm. The GIPSW is also applied in physical experiments exploring raster-angle-affected fatigue damage dynamics in 3D-printed materials. The estimated hidden-fatigue damage-time history reveals distinct crack propagation rates differentiated by the raster angles and can be used for damage prognosis and modeling the fatigue mechanisms. The critical inflection points identified in the incremental damage-time histories detect the crack growth phase transitions as early as 0.17 of the total time to failure, which can be used for early damage awareness.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47109542","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 : 2023-07-08DOI: 10.1177/14759217231184579
Goto Daiki, Inoue Tsuyoshi, Hori Takekiyo, Yabui Shota, Katayama Keiichi, Tomimatsu Shigeyuki, Heya Akira
Pumps are important machines used in rivers, social infrastructure, and industrial facilities. During long-term operation, journal bearings that support the pump shaft are subject to wear and peeling caused by liquids, including slurry. Wear and peeling can change the characteristics of journal bearings and cause abnormal shaft vibration. If wear and peeling progress, it can severely damage the pump. Thus, periodic maintenance and replacement are required. However, the frequency of periodic maintenance should be reduced as much as possible from a cost standpoint. Therefore, it is desirable to monitor the condition of the machine and perform maintenance only when necessary. In this study, the long-term vibration of a submerged journal bearing with slurry-containing water was monitored and recorded to identify the features that are important for condition monitoring and diagnosis and to interpret their contributions. First, an experimental test rig for a rotating shaft system was developed and long-term vibration data and changes in wear were recorded. A machine learning model (support vector machine (SVM)) was trained to predict the wear and damage conditions of the bearings, and its effectiveness was verified. In addition, two important features were selected as major contributors to the wear and peeling phenomena of journal bearings. These important features were interpreted using partial dependence (PD), Individual Conditional Expectation (ICE), and SHapley Additive exPlanations (SHAP), and the degree of contribution and characteristics of these features were clarified. Later, a reduced SVM model was trained using only these important features, and its effectiveness was clarified using another bearing’s data of wear and peeling processes.
{"title":"Failure diagnosis and physical interpretation of journal bearing for slurry liquid using long-term real vibration data","authors":"Goto Daiki, Inoue Tsuyoshi, Hori Takekiyo, Yabui Shota, Katayama Keiichi, Tomimatsu Shigeyuki, Heya Akira","doi":"10.1177/14759217231184579","DOIUrl":"https://doi.org/10.1177/14759217231184579","url":null,"abstract":"Pumps are important machines used in rivers, social infrastructure, and industrial facilities. During long-term operation, journal bearings that support the pump shaft are subject to wear and peeling caused by liquids, including slurry. Wear and peeling can change the characteristics of journal bearings and cause abnormal shaft vibration. If wear and peeling progress, it can severely damage the pump. Thus, periodic maintenance and replacement are required. However, the frequency of periodic maintenance should be reduced as much as possible from a cost standpoint. Therefore, it is desirable to monitor the condition of the machine and perform maintenance only when necessary. In this study, the long-term vibration of a submerged journal bearing with slurry-containing water was monitored and recorded to identify the features that are important for condition monitoring and diagnosis and to interpret their contributions. First, an experimental test rig for a rotating shaft system was developed and long-term vibration data and changes in wear were recorded. A machine learning model (support vector machine (SVM)) was trained to predict the wear and damage conditions of the bearings, and its effectiveness was verified. In addition, two important features were selected as major contributors to the wear and peeling phenomena of journal bearings. These important features were interpreted using partial dependence (PD), Individual Conditional Expectation (ICE), and SHapley Additive exPlanations (SHAP), and the degree of contribution and characteristics of these features were clarified. Later, a reduced SVM model was trained using only these important features, and its effectiveness was clarified using another bearing’s data of wear and peeling processes.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46061602","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}
Active learning is an important technology to solve the lack of data in crack detection model training. However, the sampling strategies of most existing active learning methods for crack detection are based on the uncertainty or representation of the samples, which cannot effectively balance the exploitation and exploration of active learning. To solve this problem, this study proposes an active learning method for crack detection based on subset searching and weighted sampling. First, a new active learning framework is established to successively search subsets with large uncertainty from the candidate dataset, and select training samples with large diversity from the subsets to update the crack detection model. Second, to realize the active learning process, a subset searching method based on sample relative error is proposed to adaptively select subsets with large uncertainty, and a weighted sampling method based on flow-based deep generative network is introduced to select training samples with large diversity form the subsets. Third, a termination criterion for active learning directly based on the prediction accuracy of the trained model is proposed to adaptively determine the maximum number of iterations. Finally, the proposed method is tested using two open-source crack datasets. The experimental comparison results on the Bridge Crack Library dataset show that the proposed method has higher calculation efficiency and prediction accuracy in crack detection than the uncertainty-based and representation-based active learning methods. The test results on the DeepCrack dataset show that the crack detection model trained by the proposed method has good transferability on different datasets with multi-scale concrete cracks and scenes.
{"title":"An active learning method for crack detection based on subset searching and weighted sampling","authors":"Zhengliang Xiang, Xuhui He, Yun-feng Zou, Haiquan Jing","doi":"10.1177/14759217231183661","DOIUrl":"https://doi.org/10.1177/14759217231183661","url":null,"abstract":"Active learning is an important technology to solve the lack of data in crack detection model training. However, the sampling strategies of most existing active learning methods for crack detection are based on the uncertainty or representation of the samples, which cannot effectively balance the exploitation and exploration of active learning. To solve this problem, this study proposes an active learning method for crack detection based on subset searching and weighted sampling. First, a new active learning framework is established to successively search subsets with large uncertainty from the candidate dataset, and select training samples with large diversity from the subsets to update the crack detection model. Second, to realize the active learning process, a subset searching method based on sample relative error is proposed to adaptively select subsets with large uncertainty, and a weighted sampling method based on flow-based deep generative network is introduced to select training samples with large diversity form the subsets. Third, a termination criterion for active learning directly based on the prediction accuracy of the trained model is proposed to adaptively determine the maximum number of iterations. Finally, the proposed method is tested using two open-source crack datasets. The experimental comparison results on the Bridge Crack Library dataset show that the proposed method has higher calculation efficiency and prediction accuracy in crack detection than the uncertainty-based and representation-based active learning methods. The test results on the DeepCrack dataset show that the crack detection model trained by the proposed method has good transferability on different datasets with multi-scale concrete cracks and scenes.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43305690","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 : 2023-07-08DOI: 10.1177/14759217231182303
Ruhua Wang, Yanda Shao, Qilin Li, Lingjun Li, Jun Li, Hong Hao
Conventional structural health monitoring (SHM) evaluates the condition of civil structures by analyzing the data acquired by advanced sensors. The requirement of overinvestment in specialized equipment and labor for implementation prevents the traditional SHM from large-scale usage. On the other hand, computer vision techniques offer cost-effective solutions for SHM thanks to its inherent advantage in data acquirement and processing. More importantly, it has been demonstrated that these emerging solutions can produce reliable condition diagnoses for civil structures using pure image data. In this article, a novel transformer-based neural network is proposed for vision-based structural condition assessment which is formulated to a semantic segmentation problem. The network employs Swin Transformer as the backbone and MaskFormer as the overall architecture to recognize components (sleepers, slabs, columns, etc.) and damage (concrete damage, exposed rebar) of structures. Unlike the commonly used fully convolutional networks, the proposed model tackles semantic segmentation as a mask classification rather than a pixel classification problem. To deal with the lack of training data, an image data augmentation method called Copy-Paste is extended and applied for training data generation, resulting in an increase of around 40% data for component segmentation and 71% data for damage segmentation. Experimental validations on the Tokaido railway viaduct dataset show that the proposed approach is very accurate, achieving 97% and 90% mean Intersection Over Union for component and damage segmentation, outperforming the existing methods by a significant margin. The accurate segmentation results can provide meaningful information for downstream SHM tasks.
{"title":"A novel transformer-based semantic segmentation framework for structural condition assessment","authors":"Ruhua Wang, Yanda Shao, Qilin Li, Lingjun Li, Jun Li, Hong Hao","doi":"10.1177/14759217231182303","DOIUrl":"https://doi.org/10.1177/14759217231182303","url":null,"abstract":"Conventional structural health monitoring (SHM) evaluates the condition of civil structures by analyzing the data acquired by advanced sensors. The requirement of overinvestment in specialized equipment and labor for implementation prevents the traditional SHM from large-scale usage. On the other hand, computer vision techniques offer cost-effective solutions for SHM thanks to its inherent advantage in data acquirement and processing. More importantly, it has been demonstrated that these emerging solutions can produce reliable condition diagnoses for civil structures using pure image data. In this article, a novel transformer-based neural network is proposed for vision-based structural condition assessment which is formulated to a semantic segmentation problem. The network employs Swin Transformer as the backbone and MaskFormer as the overall architecture to recognize components (sleepers, slabs, columns, etc.) and damage (concrete damage, exposed rebar) of structures. Unlike the commonly used fully convolutional networks, the proposed model tackles semantic segmentation as a mask classification rather than a pixel classification problem. To deal with the lack of training data, an image data augmentation method called Copy-Paste is extended and applied for training data generation, resulting in an increase of around 40% data for component segmentation and 71% data for damage segmentation. Experimental validations on the Tokaido railway viaduct dataset show that the proposed approach is very accurate, achieving 97% and 90% mean Intersection Over Union for component and damage segmentation, outperforming the existing methods by a significant margin. The accurate segmentation results can provide meaningful information for downstream SHM tasks.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43476915","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 : 2023-07-08DOI: 10.1177/14759217231184115
Xianzhi Li, Songtao Xue, Liyu Xie, G. Wan
This article presents a novel passive patch antenna sensor for simultaneous crack and temperature sensing, and the antenna sensor has the ability of temperature self-compensation. The passive patch antenna sensor consists of an underlying patch and an overlapping sub-patch. The off-center feeding activates resonant modes in both transverse and longitudinal directions. The resonant frequency shift in transverse direction is used for environmental temperature sensing, while the structural crack width can be sensed by the longitudinal resonant frequency shift after temperature compensation. Furthermore, the unstressed design of the antenna can also eliminate the issue of incomplete strain transfer ratios. In this article, the relationships between the antenna resonant frequencies, the environmental temperature, and the structural crack width were studied. Simulations were conducted to determine the optimal off-center fed distance of the patch antenna sensor. Furthermore, a series of experimental tests were also conducted, where the passive patch antenna was fabricated and installed on the concrete components as well as an actual building. Continuous monitoring was performed for several days to test the temperature sensing ability of the passive patch antenna, and the sensed crack width after temperature compensation was compared with the actual results. The results of these experiments demonstrate the feasibility of using the passive patch antenna for simultaneous temperature and crack sensing.
{"title":"Simultaneous crack and temperature sensing with passive patch antenna","authors":"Xianzhi Li, Songtao Xue, Liyu Xie, G. Wan","doi":"10.1177/14759217231184115","DOIUrl":"https://doi.org/10.1177/14759217231184115","url":null,"abstract":"This article presents a novel passive patch antenna sensor for simultaneous crack and temperature sensing, and the antenna sensor has the ability of temperature self-compensation. The passive patch antenna sensor consists of an underlying patch and an overlapping sub-patch. The off-center feeding activates resonant modes in both transverse and longitudinal directions. The resonant frequency shift in transverse direction is used for environmental temperature sensing, while the structural crack width can be sensed by the longitudinal resonant frequency shift after temperature compensation. Furthermore, the unstressed design of the antenna can also eliminate the issue of incomplete strain transfer ratios. In this article, the relationships between the antenna resonant frequencies, the environmental temperature, and the structural crack width were studied. Simulations were conducted to determine the optimal off-center fed distance of the patch antenna sensor. Furthermore, a series of experimental tests were also conducted, where the passive patch antenna was fabricated and installed on the concrete components as well as an actual building. Continuous monitoring was performed for several days to test the temperature sensing ability of the passive patch antenna, and the sensed crack width after temperature compensation was compared with the actual results. The results of these experiments demonstrate the feasibility of using the passive patch antenna for simultaneous temperature and crack sensing.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46569054","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 : 2023-07-08DOI: 10.1177/14759217231183656
Han Zhang, Allen A. Zhang, Anzheng He, Zishuo Dong, Yang Liu
Concurrently detecting multiple objects of interest will yield massive time savings in processing and enable a more streamlined and unified detection system. The ShuttleNet is designed to repeat the encoding–decoding round freely or even endlessly, achieving prodigious successes in terms of simultaneous detection of multiple pavement distresses and surface design features on asphalt pavements. This paper proposes an efficient and improved architecture of ShuttleNet called ShuttleNetV2 for enhanced global modeling and retrieving fine details capabilities. The proposed ShuttleNetV2 represents two major modifications on the original ShuttleNet. On the one hand, the self-attention mechanism is purposefully introduced to capture long-range dependency. On the other hand, ShuttleNetV2 adopts various sampling scales to combine the characteristics of different receptive fields. The experimental results indicate that the recommended architectural variation of the proposed ShuttleNetV2 model yields a mean F-measure of 94.21% and a mean intersection-over-union of 0.8914 on 1500 pairs of testing images. The proposed ShuttleNetV2 outperforms ShuttleNet in detecting nearly all types of pavement patterns. In particular, ShuttleNetV2 efficaciously tackles the tangible limitations of ShuttleNet in detecting giant distresses. Moreover, the ShuttleNetV2 can process an image in roughly 78 ms using modern graphic processing unit devices, which has a promising potential in supporting the real-time detection of multiple pavement distresses and surface design features on asphalt pavements.
{"title":"Pixel-level detection of multiple pavement distresses and surface design features with ShuttleNetV2","authors":"Han Zhang, Allen A. Zhang, Anzheng He, Zishuo Dong, Yang Liu","doi":"10.1177/14759217231183656","DOIUrl":"https://doi.org/10.1177/14759217231183656","url":null,"abstract":"Concurrently detecting multiple objects of interest will yield massive time savings in processing and enable a more streamlined and unified detection system. The ShuttleNet is designed to repeat the encoding–decoding round freely or even endlessly, achieving prodigious successes in terms of simultaneous detection of multiple pavement distresses and surface design features on asphalt pavements. This paper proposes an efficient and improved architecture of ShuttleNet called ShuttleNetV2 for enhanced global modeling and retrieving fine details capabilities. The proposed ShuttleNetV2 represents two major modifications on the original ShuttleNet. On the one hand, the self-attention mechanism is purposefully introduced to capture long-range dependency. On the other hand, ShuttleNetV2 adopts various sampling scales to combine the characteristics of different receptive fields. The experimental results indicate that the recommended architectural variation of the proposed ShuttleNetV2 model yields a mean F-measure of 94.21% and a mean intersection-over-union of 0.8914 on 1500 pairs of testing images. The proposed ShuttleNetV2 outperforms ShuttleNet in detecting nearly all types of pavement patterns. In particular, ShuttleNetV2 efficaciously tackles the tangible limitations of ShuttleNet in detecting giant distresses. Moreover, the ShuttleNetV2 can process an image in roughly 78 ms using modern graphic processing unit devices, which has a promising potential in supporting the real-time detection of multiple pavement distresses and surface design features on asphalt pavements.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44268200","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 : 2023-07-08DOI: 10.1177/14759217231184584
Qi Li, Jingze Gao, J. Beck, Chao Lin, Yong Huang, Hui Li
Outlier detection is an important procedure taken in structural health monitoring (SHM) to create clean and reliable data. A robust time series outlier detection method incorporating a Bayesian perspective and an extreme learning machine (ELM) neural network model is proposed, with application to long-term monitoring data of ballastless tracks for high-speed railway systems. A robust sparse Bayesian ELM (SBELM) model is first established by computing the posterior probability density function of the ELM weight parameters and then marginalizing over the prediction-error precision parameter to obtain a robust nonlinear regression model between the track temperature and structural response. Both the posterior mean and the associated uncertainties of the robust SBELM model are then taken into account to compute the outlier probability for each suspicious data point, which quantifies their degree of data “outlier-ness.” It effectively takes into account the prediction uncertainty of the SBELM regression model. The method is applied to long-term monitoring data for track temperatures, and track strain and relative displacement responses, from two high-speed rail track systems where there are both slight and serious outliers. The results demonstrate that the proposed method can reliably detect outliers by quantifying the outlier probability and that the final results are robust to the selection of the “thresholds.” It is also shown that our new algorithm produces significantly improved model prediction performance after the outliers are detected and removed.
{"title":"Probabilistic outlier detection for robust regression modeling of structural response for high-speed railway track monitoring","authors":"Qi Li, Jingze Gao, J. Beck, Chao Lin, Yong Huang, Hui Li","doi":"10.1177/14759217231184584","DOIUrl":"https://doi.org/10.1177/14759217231184584","url":null,"abstract":"Outlier detection is an important procedure taken in structural health monitoring (SHM) to create clean and reliable data. A robust time series outlier detection method incorporating a Bayesian perspective and an extreme learning machine (ELM) neural network model is proposed, with application to long-term monitoring data of ballastless tracks for high-speed railway systems. A robust sparse Bayesian ELM (SBELM) model is first established by computing the posterior probability density function of the ELM weight parameters and then marginalizing over the prediction-error precision parameter to obtain a robust nonlinear regression model between the track temperature and structural response. Both the posterior mean and the associated uncertainties of the robust SBELM model are then taken into account to compute the outlier probability for each suspicious data point, which quantifies their degree of data “outlier-ness.” It effectively takes into account the prediction uncertainty of the SBELM regression model. The method is applied to long-term monitoring data for track temperatures, and track strain and relative displacement responses, from two high-speed rail track systems where there are both slight and serious outliers. The results demonstrate that the proposed method can reliably detect outliers by quantifying the outlier probability and that the final results are robust to the selection of the “thresholds.” It is also shown that our new algorithm produces significantly improved model prediction performance after the outliers are detected and removed.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45290911","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 : 2023-07-07DOI: 10.1177/14759217231185571
Cai Yi, Le Ran, Jiayin Tang, Qiuyang Zhou, Lu Zhou
Rotating machinery is an important and easily damaged component in large-scale equipment. Under the coupling action of system components, the occurrence rate of compound faults is very high, which seriously endangers equipment safety. The vibration signals of the damaged rotating machine include equipment operation vibration information, periodic impacts, environmental noise, and even accidental impacts. To effectively extract multi-fault features from compound fault signals, a multi-period pulse detection indicator called harmonic spectrum correlation kurtosis (HSCK) is proposed in this paper. Based on this, an adaptive matching extraction strategy for multiple fault features is proposed. By introducing variational mode decomposition, an adaptive plane paving method for signal components is designed, and an enhanced cyclic frequency estimation method is proposed to pre-determine the fault characteristic frequency as a prior parameter of HSCK, so as to obtain the optimal center frequency and bandwidth of multiple resonance bands. The implementation of this strategy can obtain more periodic pulse information with a high signal-to-noise ratio. Simulation results show that the strategy is accurate and effective. The data of wheel-bearing compound fault and bearing multi-element compound fault indicate that the proposed strategy can be used for compound fault diagnosis of rotating machinery.
{"title":"Harmonic spectral correlated kurtosis and an adaptive matching extraction strategy of multi-fault features for rotating machinery","authors":"Cai Yi, Le Ran, Jiayin Tang, Qiuyang Zhou, Lu Zhou","doi":"10.1177/14759217231185571","DOIUrl":"https://doi.org/10.1177/14759217231185571","url":null,"abstract":"Rotating machinery is an important and easily damaged component in large-scale equipment. Under the coupling action of system components, the occurrence rate of compound faults is very high, which seriously endangers equipment safety. The vibration signals of the damaged rotating machine include equipment operation vibration information, periodic impacts, environmental noise, and even accidental impacts. To effectively extract multi-fault features from compound fault signals, a multi-period pulse detection indicator called harmonic spectrum correlation kurtosis (HSCK) is proposed in this paper. Based on this, an adaptive matching extraction strategy for multiple fault features is proposed. By introducing variational mode decomposition, an adaptive plane paving method for signal components is designed, and an enhanced cyclic frequency estimation method is proposed to pre-determine the fault characteristic frequency as a prior parameter of HSCK, so as to obtain the optimal center frequency and bandwidth of multiple resonance bands. The implementation of this strategy can obtain more periodic pulse information with a high signal-to-noise ratio. Simulation results show that the strategy is accurate and effective. The data of wheel-bearing compound fault and bearing multi-element compound fault indicate that the proposed strategy can be used for compound fault diagnosis of rotating machinery.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46609064","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 : 2023-07-06DOI: 10.1177/14759217231178161
C. Zhang, Rupeng Zhu, Dan Wang, P. Cao, Jun Yu Li, Jun Yu Li, Pengkun Li
The ballistic impact damage (BID) will change the dynamic characteristics of the horizontal tail drive shaft system (HTDSS) and will directly affect the flight safety of the helicopter. Aiming at the problem of how the BID affects the dynamic characteristics of the HTDSS and how to identify the BID in time, the dynamic characteristics of the HTDSS with the BID are studied in this paper. The BID is simplified as the ideal geometric damage, and the calculation method of the BID is given. The finite element dynamic equation of the HTDSS with the BID is established, and the effect mechanism of the ballistic impact parameters on the dynamic characteristics is revealed. The result shows: the BID causes the mass loss and the stiffness asymmetric of the ballistic impact position of the TDS; the eccentric excitation introduced by the BID leads to the obvious increase of the system response, and the stiffness asymmetry leads to the super-harmonic resonance of the system. The obvious increase of system response and the appearance of 2Ω frequency component can be used as the identification signal of the BID. Finally, the experiment was carried out, which verified the correctness of the established dynamic model, and explained the reliability of the proposed identification signal of the BID.
{"title":"Dynamic characteristics analysis and the identification signal of the horizontal tail drive shaft system with the ballistic impact damage of a helicopter","authors":"C. Zhang, Rupeng Zhu, Dan Wang, P. Cao, Jun Yu Li, Jun Yu Li, Pengkun Li","doi":"10.1177/14759217231178161","DOIUrl":"https://doi.org/10.1177/14759217231178161","url":null,"abstract":"The ballistic impact damage (BID) will change the dynamic characteristics of the horizontal tail drive shaft system (HTDSS) and will directly affect the flight safety of the helicopter. Aiming at the problem of how the BID affects the dynamic characteristics of the HTDSS and how to identify the BID in time, the dynamic characteristics of the HTDSS with the BID are studied in this paper. The BID is simplified as the ideal geometric damage, and the calculation method of the BID is given. The finite element dynamic equation of the HTDSS with the BID is established, and the effect mechanism of the ballistic impact parameters on the dynamic characteristics is revealed. The result shows: the BID causes the mass loss and the stiffness asymmetric of the ballistic impact position of the TDS; the eccentric excitation introduced by the BID leads to the obvious increase of the system response, and the stiffness asymmetry leads to the super-harmonic resonance of the system. The obvious increase of system response and the appearance of 2Ω frequency component can be used as the identification signal of the BID. Finally, the experiment was carried out, which verified the correctness of the established dynamic model, and explained the reliability of the proposed identification signal of the BID.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"24 3-4","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41308847","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}
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