An extensive network of pipelines is extensively employed worldwide to carry oil and gas fluids over millions of kilometers. The pipeline channel was constructed to resist environmental dynamic loading conditions to provide safe and reliable oil and gas fluids transportation from the production well sites to depot stations installed at sea coastlines. However, pipeline infrastructure damages such as fractures, cracks, leakages, etc., are significant sources of economic losses in pipeline operations. Moreover, pipeline failures can cause considerable ecological catastrophes, human deaths, and financial loss. Important research initiatives have been committed to establishing pipeline breach detection and localization using various techniques to avoid these threats and maintain a secure and dependable pipeline network. This paper reviews different state-of-the-art damage detection methods and their recent advancement with a case study explaining the application of light detection and ranging for pipeline damage detection. The pros and cons of diverse damage detection methods in pipeline networks are also discussed. Research gaps for pipeline damage detection systems are also provided for better understanding and future research.
{"title":"A comprehensive study of techniques utilized for structural health monitoring of oil and gas pipelines","authors":"Vinamra Bhushan Sharma, Saurabh Tewari, Susham Biswas, Ashutosh Sharma","doi":"10.1177/14759217231183715","DOIUrl":"https://doi.org/10.1177/14759217231183715","url":null,"abstract":"An extensive network of pipelines is extensively employed worldwide to carry oil and gas fluids over millions of kilometers. The pipeline channel was constructed to resist environmental dynamic loading conditions to provide safe and reliable oil and gas fluids transportation from the production well sites to depot stations installed at sea coastlines. However, pipeline infrastructure damages such as fractures, cracks, leakages, etc., are significant sources of economic losses in pipeline operations. Moreover, pipeline failures can cause considerable ecological catastrophes, human deaths, and financial loss. Important research initiatives have been committed to establishing pipeline breach detection and localization using various techniques to avoid these threats and maintain a secure and dependable pipeline network. This paper reviews different state-of-the-art damage detection methods and their recent advancement with a case study explaining the application of light detection and ranging for pipeline damage detection. The pros and cons of diverse damage detection methods in pipeline networks are also discussed. Research gaps for pipeline damage detection systems are also provided for better understanding and future research.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136101641","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 : 2023-09-21DOI: 10.1177/14759217231192058
Gao Shang, Jun Chen
Detecting concrete internal defects through deep learning analysis of impact echo signals faces two challenges: (1) the traditional signal processing method such as wavelet transform (WT) fails to reflect data-sensitive damage characteristics due to the uncertainty principle and (2) the limited labeled data acquired from real structures impedes network training. To address the first challenge, this paper proposes the WT-based synchrosqueezing transform (WT-SST) for the conversion of time-series data to the time-frequency spectrogram, which can provide effective features for the network in time and frequency domains simultaneously. To overcome the second challenge, numerical simulation data are supplemented for the augment of labeled data. To minimize the effect of data variance between experiments and simulations, this paper uses an unsupervised domain adaptation (DA) network for the transfer training of labeled simulation data (original domain) and unlabeled experimental data (target domain). The DA network extracts domain-invariant features by maximizing the domain recognition error and minimizing the probability distribution distance. The damage probability is calculated by the trained model to produce a 2D defect contour image of concrete specimens, and the three-dimensional visualization of internal defects by estimating the defect depth based on the defect area of contour image. Finally, the recognition precision, recall, F1-score, and accuracy of the model of unsupervised DA network trained by a hybrid dataset reaches 89.4%, 88.4%, 88.9%, and 94.7%, respectively.
{"title":"Visualization of concrete internal defects based on unsupervised domain adaptation algorithm for transfer learning of experiment-simulation hybrid dataset of impact echo signals","authors":"Gao Shang, Jun Chen","doi":"10.1177/14759217231192058","DOIUrl":"https://doi.org/10.1177/14759217231192058","url":null,"abstract":"Detecting concrete internal defects through deep learning analysis of impact echo signals faces two challenges: (1) the traditional signal processing method such as wavelet transform (WT) fails to reflect data-sensitive damage characteristics due to the uncertainty principle and (2) the limited labeled data acquired from real structures impedes network training. To address the first challenge, this paper proposes the WT-based synchrosqueezing transform (WT-SST) for the conversion of time-series data to the time-frequency spectrogram, which can provide effective features for the network in time and frequency domains simultaneously. To overcome the second challenge, numerical simulation data are supplemented for the augment of labeled data. To minimize the effect of data variance between experiments and simulations, this paper uses an unsupervised domain adaptation (DA) network for the transfer training of labeled simulation data (original domain) and unlabeled experimental data (target domain). The DA network extracts domain-invariant features by maximizing the domain recognition error and minimizing the probability distribution distance. The damage probability is calculated by the trained model to produce a 2D defect contour image of concrete specimens, and the three-dimensional visualization of internal defects by estimating the defect depth based on the defect area of contour image. Finally, the recognition precision, recall, F1-score, and accuracy of the model of unsupervised DA network trained by a hybrid dataset reaches 89.4%, 88.4%, 88.9%, and 94.7%, respectively.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136152627","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 : 2023-09-20DOI: 10.1177/14759217231195275
Yang Li, Xiangyin Meng, Shide Xiao, Feiyun Xu, Chi-Guhn Lee
Due to the harsh working environment of hoisting machinery system, the fault information of the important components is significantly complex, which leads to the fault signals not being collected completely by using only single channel. To alleviate this problem, acoustic emission (AE) experiments are applied to collect multichannel AE signal of hoisting machinery system. Additionally, a new intelligent fault diagnosis method based on multivariate variational mode decomposition (MVMD) and generalized composite multiscale permutation entropy (GCMPE) is proposed to extract multichannel AE fault features and implement multichannel fault diagnosis of hoisting machinery system. Firstly, based on variational mode decomposition (VMD) and the idea of multichannel AE data processing, MVMD is proposed to process the original multichannel AE signals collected from hoisting machinery system, which can obtain adaptively several multichannel modal components containing discriminative information. Meanwhile, GCMPE is presented to extract the fault information of multichannel modal components obtained by MVMD, which can improve the feature extraction performance of the original multiscale permutation entropy. The experimental results demonstrate the effectiveness and superiority of the proposed method in multichannel fault diagnosis of hoisting machinery system compared with some traditional single-channel analysis and other multichannel analysis methods.
{"title":"Multivariate variational mode decomposition and generalized composite multiscale permutation entropy for multichannel fault diagnosis of hoisting machinery system","authors":"Yang Li, Xiangyin Meng, Shide Xiao, Feiyun Xu, Chi-Guhn Lee","doi":"10.1177/14759217231195275","DOIUrl":"https://doi.org/10.1177/14759217231195275","url":null,"abstract":"Due to the harsh working environment of hoisting machinery system, the fault information of the important components is significantly complex, which leads to the fault signals not being collected completely by using only single channel. To alleviate this problem, acoustic emission (AE) experiments are applied to collect multichannel AE signal of hoisting machinery system. Additionally, a new intelligent fault diagnosis method based on multivariate variational mode decomposition (MVMD) and generalized composite multiscale permutation entropy (GCMPE) is proposed to extract multichannel AE fault features and implement multichannel fault diagnosis of hoisting machinery system. Firstly, based on variational mode decomposition (VMD) and the idea of multichannel AE data processing, MVMD is proposed to process the original multichannel AE signals collected from hoisting machinery system, which can obtain adaptively several multichannel modal components containing discriminative information. Meanwhile, GCMPE is presented to extract the fault information of multichannel modal components obtained by MVMD, which can improve the feature extraction performance of the original multiscale permutation entropy. The experimental results demonstrate the effectiveness and superiority of the proposed method in multichannel fault diagnosis of hoisting machinery system compared with some traditional single-channel analysis and other multichannel analysis methods.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136309273","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 : 2023-09-15DOI: 10.1177/14759217231184276
Dong-Hui Yang, Ze-Xin Guan, Ting-Hua Yi, Hong-Nan Li, Hua Liu
The structural temperature gradient (STG) is one of the most key factors causing cracking and even damage to bridge structures. However, its real effects on bridge structures are often over- or underestimated in practice. For most operating bridges, the structural health monitoring systems have just been put into use recently, and the monitoring structural temperature data are limited, which always leads to unreasonable STG representative value for a long return period based on such short-term structural temperature data. To solve the problems, this article proposes an STG determination method based on the long-term historical meteorological parameters at bridge sites. First, the main meteorological parameters affecting the STG were determined by correlation analysis. Second, considering the different influence mechanisms of various meteorological conditions on STG, a training sample set construction method is proposed by clustering the meteorological parameters and STG monitoring data. Based on such training data, a correlation model between STG and meteorological parameters can be established to extend the STG dataset based on the historical meteorological data. Finally, the peak over threshold method is applied to analyze the obtained extended STG data and to estimate its representative value. The proposed method was verified through a long-span cable-stayed bridge. The results show that the monitoring dataset of the STG can be effectively extended through the established correlation model. Compared with the short-term monitoring data, more reasonable representative values of the STG can be obtained through the extended dataset of monitoring STG.
{"title":"Structural temperature gradient evaluation based on bridge monitoring data extended by historical meteorological data","authors":"Dong-Hui Yang, Ze-Xin Guan, Ting-Hua Yi, Hong-Nan Li, Hua Liu","doi":"10.1177/14759217231184276","DOIUrl":"https://doi.org/10.1177/14759217231184276","url":null,"abstract":"The structural temperature gradient (STG) is one of the most key factors causing cracking and even damage to bridge structures. However, its real effects on bridge structures are often over- or underestimated in practice. For most operating bridges, the structural health monitoring systems have just been put into use recently, and the monitoring structural temperature data are limited, which always leads to unreasonable STG representative value for a long return period based on such short-term structural temperature data. To solve the problems, this article proposes an STG determination method based on the long-term historical meteorological parameters at bridge sites. First, the main meteorological parameters affecting the STG were determined by correlation analysis. Second, considering the different influence mechanisms of various meteorological conditions on STG, a training sample set construction method is proposed by clustering the meteorological parameters and STG monitoring data. Based on such training data, a correlation model between STG and meteorological parameters can be established to extend the STG dataset based on the historical meteorological data. Finally, the peak over threshold method is applied to analyze the obtained extended STG data and to estimate its representative value. The proposed method was verified through a long-span cable-stayed bridge. The results show that the monitoring dataset of the STG can be effectively extended through the established correlation model. Compared with the short-term monitoring data, more reasonable representative values of the STG can be obtained through the extended dataset of monitoring STG.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"356 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135396899","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 : 2023-09-08DOI: 10.1177/14759217231193088
A. Mendler, Michael Döhler, Christian U. Grosse
This paper develops a model-assisted approach for determining predictive probability of detection curves. The approach is “model-assisted,” as the damage-sensitive features are evaluated in combination with a numerical model of the examined structure. It is “predictive” in the sense that probability of detection (POD) curves can be constructed based on measurement records from the undamaged structure, avoiding any destructive tests. The approach can be applied to a wide range of damage-sensitive features in structural health monitoring and non-destructive testing, provided the statistical distribution of the features can be approximated by a normal distribution. In particular, it is suitable for global vibration-based features, such as modal parameters, and evaluates changes in local structural components, for example, changes in material properties, cross-sectional values, prestressing forces, and support conditions. The approach explicitly considers the statistical uncertainties of the features due to measurement noise, unknown excitation, or other noise sources. Moreover, through confidence intervals, it considers model-based uncertainties due to uncertain structural parameters and a possible mismatch between the modeled and the real structure. Experimental studies based on a laboratory beam structure demonstrate that the approach can predict the POD before damage occurs. Ultimately, several ways to utilize predictive POD curves are discussed, for example, for the evaluation of the most suitable measurement equipment, for quality control, for feature selection, or sensor placement optimization.
{"title":"Predictive probability of detection curves based on data from undamaged structures","authors":"A. Mendler, Michael Döhler, Christian U. Grosse","doi":"10.1177/14759217231193088","DOIUrl":"https://doi.org/10.1177/14759217231193088","url":null,"abstract":"This paper develops a model-assisted approach for determining predictive probability of detection curves. The approach is “model-assisted,” as the damage-sensitive features are evaluated in combination with a numerical model of the examined structure. It is “predictive” in the sense that probability of detection (POD) curves can be constructed based on measurement records from the undamaged structure, avoiding any destructive tests. The approach can be applied to a wide range of damage-sensitive features in structural health monitoring and non-destructive testing, provided the statistical distribution of the features can be approximated by a normal distribution. In particular, it is suitable for global vibration-based features, such as modal parameters, and evaluates changes in local structural components, for example, changes in material properties, cross-sectional values, prestressing forces, and support conditions. The approach explicitly considers the statistical uncertainties of the features due to measurement noise, unknown excitation, or other noise sources. Moreover, through confidence intervals, it considers model-based uncertainties due to uncertain structural parameters and a possible mismatch between the modeled and the real structure. Experimental studies based on a laboratory beam structure demonstrate that the approach can predict the POD before damage occurs. Ultimately, several ways to utilize predictive POD curves are discussed, for example, for the evaluation of the most suitable measurement equipment, for quality control, for feature selection, or sensor placement optimization.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46139772","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 : 2023-09-07DOI: 10.1177/14759217231192003
MingAng Guo, Xiaotong Tu, Saqlain Abbas, Shuangmu Zhuo, Xiaolu Li
Mechanical system condition monitoring is an important procedure in modern industry, which not only reduces maintenance costs but also ensures safe equipment operation. At present, the monitoring method based on signal processing is one of the most common and effective fault diagnosis methods. In this work, the time-frequency distribution (TFD) obtained by generalized horizontal synchrosqueezing transform is used to extract the impulse feature of the non-stationary vibration signal of the tool. By using the TFD result, the two-dimensional (2D) Fourier transform can further detect the periodic pulses. Next, the energy proportion factor of periodic frequency point is proposed to evaluate the different tool wear degrees. Numerical simulations and experimental data analysis demonstrate the effectiveness of the proposed method as well as the potential for condition monitoring.
{"title":"Time-frequency analysis-based impulse feature extraction method for quantitative evaluation of milling tool wear","authors":"MingAng Guo, Xiaotong Tu, Saqlain Abbas, Shuangmu Zhuo, Xiaolu Li","doi":"10.1177/14759217231192003","DOIUrl":"https://doi.org/10.1177/14759217231192003","url":null,"abstract":"Mechanical system condition monitoring is an important procedure in modern industry, which not only reduces maintenance costs but also ensures safe equipment operation. At present, the monitoring method based on signal processing is one of the most common and effective fault diagnosis methods. In this work, the time-frequency distribution (TFD) obtained by generalized horizontal synchrosqueezing transform is used to extract the impulse feature of the non-stationary vibration signal of the tool. By using the TFD result, the two-dimensional (2D) Fourier transform can further detect the periodic pulses. Next, the energy proportion factor of periodic frequency point is proposed to evaluate the different tool wear degrees. Numerical simulations and experimental data analysis demonstrate the effectiveness of the proposed method as well as the potential for condition monitoring.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49574581","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 : 2023-09-07DOI: 10.1177/14759217231192363
Maoyou Ye, Xiaoan Yan, Ning Chen, Ying Liu
Due to adverse working conditions of rotating machinery in actual engineering, bearing fault data are more difficult to acquire compared to normal data. That said, the real collected bearing vibration data are usually characterized by imbalance. Meanwhile, fault information of the raw collected bearing vibration data is effortlessly drowned out by strong noises, which indicates that it is awkward to efficiently recognize bearing fault states via using traditional fault diagnosis methods under this background. To overcome these problems, this research proposes an individual approach formally intituled as robust multi-scale learning network (RMSLN) with quasi-hyperbolic momentum-based Adam (QHAdam) optimizer for bearing fault diagnosis, which mainly includes convolution-pooling operation, multi-scale branch, and classification layer. Within the proposed method, the channel attention mechanism based on squeezed excitation network is embedded into the multi-scale branch in the form of residual connections, which not only reinforce important information and weaken noise interference, but also capture fault features more comprehensively and enhance the discrimination of fault states with fewer samples. Additionally, in the training process, QHAdam optimizer is introduced to tightly control the loss of RMSLN to enable a faster and smoother convergence. Two groups of experimental bearing data are studied to support the availability of presented approach, and several traditional fault diagnosis methods and representative imbalance fault diagnosis approaches are compared in four evaluation metrics (accuracy, macro-precision, macro-recall, and macro-F1 score) to highlight the advantages of the presented method.
{"title":"A robust multi-scale learning network with quasi-hyperbolic momentum-based Adam optimizer for bearing intelligent fault diagnosis under sample imbalance scenarios and strong noise environment","authors":"Maoyou Ye, Xiaoan Yan, Ning Chen, Ying Liu","doi":"10.1177/14759217231192363","DOIUrl":"https://doi.org/10.1177/14759217231192363","url":null,"abstract":"Due to adverse working conditions of rotating machinery in actual engineering, bearing fault data are more difficult to acquire compared to normal data. That said, the real collected bearing vibration data are usually characterized by imbalance. Meanwhile, fault information of the raw collected bearing vibration data is effortlessly drowned out by strong noises, which indicates that it is awkward to efficiently recognize bearing fault states via using traditional fault diagnosis methods under this background. To overcome these problems, this research proposes an individual approach formally intituled as robust multi-scale learning network (RMSLN) with quasi-hyperbolic momentum-based Adam (QHAdam) optimizer for bearing fault diagnosis, which mainly includes convolution-pooling operation, multi-scale branch, and classification layer. Within the proposed method, the channel attention mechanism based on squeezed excitation network is embedded into the multi-scale branch in the form of residual connections, which not only reinforce important information and weaken noise interference, but also capture fault features more comprehensively and enhance the discrimination of fault states with fewer samples. Additionally, in the training process, QHAdam optimizer is introduced to tightly control the loss of RMSLN to enable a faster and smoother convergence. Two groups of experimental bearing data are studied to support the availability of presented approach, and several traditional fault diagnosis methods and representative imbalance fault diagnosis approaches are compared in four evaluation metrics (accuracy, macro-precision, macro-recall, and macro-F1 score) to highlight the advantages of the presented method.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47037290","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 : 2023-09-07DOI: 10.1177/14759217231191417
R. Roig, X. Sánchez-Botello, O. de la Torre, Xavier Ayneto, C. Högström, B. Mulu, X. Escaler
The current renewable energy market forces hydraulic turbines to operate for longer periods of time at off-design and transient conditions. Their life expectancy is then decreased due to the wear provoked by flow instabilities and stochastic flow excitations. This study presents an experimental investigation into the fatigue damage induced on the runner blades of a Kaplan turbine model when working at speed-no-load (SNL), part load (PL) and during ramps of load. The unit was equipped with on-board sensors on the blades and the shaft as well as with off-board sensors installed on the supporting structure and the draft tube cone. The results reveal that operation at SNL induces more fatigue damage on the runner blades than at PL. The damage is then mainly induced by stochastic flow excitations at SNL and by the rotating mode of the rotating vortex rope (RVR) at PL. The ramps of load, when crossing each operating condition, cause levels of damage similar to those found in stationary operation. Finally, it was proved that the blade fatigue damage can be estimated from on-board shaft measurements at any condition. However, the blade fatigue damage can only be estimated using off-board measurements when the RVR is fully developed at PL.
{"title":"Fatigue damage analysis of a Kaplan turbine model operating at off-design and transient conditions","authors":"R. Roig, X. Sánchez-Botello, O. de la Torre, Xavier Ayneto, C. Högström, B. Mulu, X. Escaler","doi":"10.1177/14759217231191417","DOIUrl":"https://doi.org/10.1177/14759217231191417","url":null,"abstract":"The current renewable energy market forces hydraulic turbines to operate for longer periods of time at off-design and transient conditions. Their life expectancy is then decreased due to the wear provoked by flow instabilities and stochastic flow excitations. This study presents an experimental investigation into the fatigue damage induced on the runner blades of a Kaplan turbine model when working at speed-no-load (SNL), part load (PL) and during ramps of load. The unit was equipped with on-board sensors on the blades and the shaft as well as with off-board sensors installed on the supporting structure and the draft tube cone. The results reveal that operation at SNL induces more fatigue damage on the runner blades than at PL. The damage is then mainly induced by stochastic flow excitations at SNL and by the rotating mode of the rotating vortex rope (RVR) at PL. The ramps of load, when crossing each operating condition, cause levels of damage similar to those found in stationary operation. Finally, it was proved that the blade fatigue damage can be estimated from on-board shaft measurements at any condition. However, the blade fatigue damage can only be estimated using off-board measurements when the RVR is fully developed at PL.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41652542","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 : 2023-09-06DOI: 10.1177/14759217231189426
Qianchen Sun, Mohammed Zeb Elshafie, Xiaomin Xu, Jennifer Schooling
Thermal integrity testing has been successfully used to assess the quality of cast-in-place piles for the past decade. It employs temperature data measured during concrete curing to identify defects along the piles’ length. However, the uptake of this technology has been rather limited in the piling industry. The main concerns are that the method is not standardised and its reliability is not well understood. In order to address these, there are a number of fundamental questions that need to be explored in more detail, including (a) the optimum time to conduct the assessment, (b) the defect thermal impact, (c) the zone of influence on temperature sensors, (d) the minimum detectable size of a defect and (e) the associated optimum sensor location required. In this paper, experimental and numerical studies were conducted to examine these questions. Fibre optic sensors were employed on model concrete piles in laboratory tests to provide fully distributed temperature data throughout the curing process. The test results showed that the optimum time to assess the defects is approximately at 60% of the time to reach peak temperature and the minimal detectable defect size, using the currently available optical fibre sensor technology, is 4% of the cross-sectional area. In addition, the thermal influence of different defect sizes is presented. Following this, it is shown in the paper that the minimum numbers of sensor cables required to identify defects with cross-sectional areas of 4%, 5% and 8% are eight, six and four cables, respectively. The optimum layout of these sensor cables within a pile cross-section has also been discussed. When specifying pile instrumentation for integrity assessment, the findings of this paper enable practising engineers to make informed judgements in relation to the size of defects they would like to detect (and hence the associated risk this entails) together with the corresponding instrumentation layout required.
{"title":"Pile defect assessment using distributed temperature sensing: fundamental questions examined","authors":"Qianchen Sun, Mohammed Zeb Elshafie, Xiaomin Xu, Jennifer Schooling","doi":"10.1177/14759217231189426","DOIUrl":"https://doi.org/10.1177/14759217231189426","url":null,"abstract":"Thermal integrity testing has been successfully used to assess the quality of cast-in-place piles for the past decade. It employs temperature data measured during concrete curing to identify defects along the piles’ length. However, the uptake of this technology has been rather limited in the piling industry. The main concerns are that the method is not standardised and its reliability is not well understood. In order to address these, there are a number of fundamental questions that need to be explored in more detail, including (a) the optimum time to conduct the assessment, (b) the defect thermal impact, (c) the zone of influence on temperature sensors, (d) the minimum detectable size of a defect and (e) the associated optimum sensor location required. In this paper, experimental and numerical studies were conducted to examine these questions. Fibre optic sensors were employed on model concrete piles in laboratory tests to provide fully distributed temperature data throughout the curing process. The test results showed that the optimum time to assess the defects is approximately at 60% of the time to reach peak temperature and the minimal detectable defect size, using the currently available optical fibre sensor technology, is 4% of the cross-sectional area. In addition, the thermal influence of different defect sizes is presented. Following this, it is shown in the paper that the minimum numbers of sensor cables required to identify defects with cross-sectional areas of 4%, 5% and 8% are eight, six and four cables, respectively. The optimum layout of these sensor cables within a pile cross-section has also been discussed. When specifying pile instrumentation for integrity assessment, the findings of this paper enable practising engineers to make informed judgements in relation to the size of defects they would like to detect (and hence the associated risk this entails) together with the corresponding instrumentation layout required.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48797251","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 : 2023-09-02DOI: 10.1177/14759217231199536
{"title":"Corrigendum to CrackDenseLinkNet: a deep convolutional neural network for semantic segmentation of cracks on concrete surface images","authors":"","doi":"10.1177/14759217231199536","DOIUrl":"https://doi.org/10.1177/14759217231199536","url":null,"abstract":"","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44508510","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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