Pub Date : 2023-07-05DOI: 10.1177/14759217231181419
Song-hui Li, Guoqing Liu, Yan Zhang, Hongbo Zhao, S. Feng, Fanzi Wu
The structural form of sandwich-structured immersed tunnel (SSIT) can be complex. During the casting of self-compacting concrete, creating void defects between the steel shell and concrete interface is not difficult, which can adversely affect the overall safety and service life of the structure. However, detecting millimeter-scale voids covered by a thick steel plate is a technical challenge for current engineering industries. In this study, we proposed a nondestructive millimeter-scale void detection method for SSITs with thick steel shells by combining impact imaging and neutron methods. First, based on the near-source wavefield theory and count rate of thermal neutrons, the void area and depth calculation methods were derived theoretically, and then the coupling detection method and grading criteria for void severity were proposed. Additionally, the void detection performance was validated for a full-scale SSIT model test by blind detection. Finally, the proposed method was applied to the SSIT of the Shenzhen–Zhongshan bridge. The results showed that the proposed method could quantitatively determine the location and distribution pattern of a void; however, it could not accurately determine the void depth. In contrast, the neutron method could accurately calculate the void depth but had a large minimum detectable unit area. The proposed method could effectively compensate for the limitations of both methods. Statistically, the coincidence rate of the model test was 95%, 89%, and 87.5% for the void location, void area, and void depth, respectively, when the error range was ±2 mm. Using this method, 30 tubes in the Shenzhen–Zhongshan bridge were inspected, and by summarizing the void law, suggestions to improve the casting process were proposed, such as adjusting the casting speed. Meanwhile, the void probability decreased significantly. The proposed method provides an important basis for high-quality construction in SSIT projects.
{"title":"Nondestructive millimeter-scale void detection for thick steel-shell–concrete interface of immersed tube tunnel: case study","authors":"Song-hui Li, Guoqing Liu, Yan Zhang, Hongbo Zhao, S. Feng, Fanzi Wu","doi":"10.1177/14759217231181419","DOIUrl":"https://doi.org/10.1177/14759217231181419","url":null,"abstract":"The structural form of sandwich-structured immersed tunnel (SSIT) can be complex. During the casting of self-compacting concrete, creating void defects between the steel shell and concrete interface is not difficult, which can adversely affect the overall safety and service life of the structure. However, detecting millimeter-scale voids covered by a thick steel plate is a technical challenge for current engineering industries. In this study, we proposed a nondestructive millimeter-scale void detection method for SSITs with thick steel shells by combining impact imaging and neutron methods. First, based on the near-source wavefield theory and count rate of thermal neutrons, the void area and depth calculation methods were derived theoretically, and then the coupling detection method and grading criteria for void severity were proposed. Additionally, the void detection performance was validated for a full-scale SSIT model test by blind detection. Finally, the proposed method was applied to the SSIT of the Shenzhen–Zhongshan bridge. The results showed that the proposed method could quantitatively determine the location and distribution pattern of a void; however, it could not accurately determine the void depth. In contrast, the neutron method could accurately calculate the void depth but had a large minimum detectable unit area. The proposed method could effectively compensate for the limitations of both methods. Statistically, the coincidence rate of the model test was 95%, 89%, and 87.5% for the void location, void area, and void depth, respectively, when the error range was ±2 mm. Using this method, 30 tubes in the Shenzhen–Zhongshan bridge were inspected, and by summarizing the void law, suggestions to improve the casting process were proposed, such as adjusting the casting speed. Meanwhile, the void probability decreased significantly. The proposed method provides an important basis for high-quality construction in SSIT projects.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49457841","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 : 2023-07-03DOI: 10.1177/14759217231175886
C. Zhang, Long Zhang
There are few studies on the fault diagnosis of deep learning in real large-scale bearings, such as wind turbine pitch bearings. We present a novel fault diagnosis method, Bayesian augmented temporal convolutional network (BATCN), to filter the raw signal in wind turbine pitch bearing defect detection. This method, which employs temporal convolutional neural networks, is designed to capture the temporal dependencies of the signal, with such a focus on non-stationary relationships in the collected signals. By referring to the thoughts of Bayesian optimization, our approach can spontaneously find the best patch length that influences fault signal extraction during the filtering process, avoiding manual tuning of this hyper-parameter. This BATCN method is first performed on simulation signals and an open-source dataset of general bearings, and then validated on industrial wind turbine pitch bearings both in the lab and in the real wind farm, where the bearings have been operated for over 15 years. The results show that our method can work well for large-scale slow-speed wind turbine pitch bearings.
{"title":"Wind turbine pitch bearing fault detection with Bayesian augmented temporal convolutional networks","authors":"C. Zhang, Long Zhang","doi":"10.1177/14759217231175886","DOIUrl":"https://doi.org/10.1177/14759217231175886","url":null,"abstract":"There are few studies on the fault diagnosis of deep learning in real large-scale bearings, such as wind turbine pitch bearings. We present a novel fault diagnosis method, Bayesian augmented temporal convolutional network (BATCN), to filter the raw signal in wind turbine pitch bearing defect detection. This method, which employs temporal convolutional neural networks, is designed to capture the temporal dependencies of the signal, with such a focus on non-stationary relationships in the collected signals. By referring to the thoughts of Bayesian optimization, our approach can spontaneously find the best patch length that influences fault signal extraction during the filtering process, avoiding manual tuning of this hyper-parameter. This BATCN method is first performed on simulation signals and an open-source dataset of general bearings, and then validated on industrial wind turbine pitch bearings both in the lab and in the real wind farm, where the bearings have been operated for over 15 years. The results show that our method can work well for large-scale slow-speed wind turbine pitch bearings.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45368062","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 : 2023-07-03DOI: 10.1177/14759217231181514
Yonghao Miao, Huifang Shi, Chenhui Li, J. Hua, Jingyi Lin
Deconvolution methods have been widely used in machinery fault diagnosis. However, their application would be confined due to the heavy noise and complex interference since the fault feature in the measured signal becomes rather weak. Time synchronous averaging (TSA) can enhance the periodic components and suppress the others by the comb filter function. And in the iteration process of the deconvolution methods, the filtered signal after each iteration can be further processed using TSA, and the time delay with maximum Gini index value is refined as the iterative period for the next iteration. Benefitting from these advantages, a period-refined maximum second-order cyclostationarity blind deconvolution (PRCYCBD) using TSA is proposed for the weak fault detection of rolling element bearings (REBs) in this paper. Firstly, without any prior knowledge, the proposed method which can estimate the period more accurately is more suitable for the weak fault detection of REBs, especially incipient fault. Secondly, TSA is firstly applied to estimate the iterative period rather than just depending on the Signal Noise Ratio (SNR) of the filtered signal in the iterative process . Furthermore, the new improvement frame can be expanded to other deconvolution methods using iterative algorithms, especially under heavy noise. Finally, a simulation with a slight bearing fault as well as two real experimental data including the vibration signal with the wind turbine bearing fault and the acoustical signal with the locomotive wheel bearing fault is used to verify the superiority of the proposed PRCYCBD compared with the traditional minimum entropy deconvolution and the traditional autocorrelation-improved cyclostationarity blind deconvolution.
{"title":"Period-refined CYCBD using time synchronous averaging for the feature extraction of bearing fault under heavy noise","authors":"Yonghao Miao, Huifang Shi, Chenhui Li, J. Hua, Jingyi Lin","doi":"10.1177/14759217231181514","DOIUrl":"https://doi.org/10.1177/14759217231181514","url":null,"abstract":"Deconvolution methods have been widely used in machinery fault diagnosis. However, their application would be confined due to the heavy noise and complex interference since the fault feature in the measured signal becomes rather weak. Time synchronous averaging (TSA) can enhance the periodic components and suppress the others by the comb filter function. And in the iteration process of the deconvolution methods, the filtered signal after each iteration can be further processed using TSA, and the time delay with maximum Gini index value is refined as the iterative period for the next iteration. Benefitting from these advantages, a period-refined maximum second-order cyclostationarity blind deconvolution (PRCYCBD) using TSA is proposed for the weak fault detection of rolling element bearings (REBs) in this paper. Firstly, without any prior knowledge, the proposed method which can estimate the period more accurately is more suitable for the weak fault detection of REBs, especially incipient fault. Secondly, TSA is firstly applied to estimate the iterative period rather than just depending on the Signal Noise Ratio (SNR) of the filtered signal in the iterative process . Furthermore, the new improvement frame can be expanded to other deconvolution methods using iterative algorithms, especially under heavy noise. Finally, a simulation with a slight bearing fault as well as two real experimental data including the vibration signal with the wind turbine bearing fault and the acoustical signal with the locomotive wheel bearing fault is used to verify the superiority of the proposed PRCYCBD compared with the traditional minimum entropy deconvolution and the traditional autocorrelation-improved cyclostationarity blind deconvolution.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48978213","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 : 2023-07-03DOI: 10.1177/14759217231181882
Xiaoyu Gong, Xiaodong Song, C. Cai, Guangqi Li, Wen Xiong
The stiffness degeneration of small to medium span bridges has been increasingly observed in recent years, and it has become a major concern of government and bridge owners. A quantitative evaluation method for the bridge performance with strengthening measures is highly desired. Due to the advantages of uninterrupted traffic and long-term tracking capability, a temperature-driven approach for characterization of the correlation pattern between bridge temperature-induced strains and bridge status was proposed in the present study by using structural health monitoring data. First, a theoretical solution of the simplified bridge model was derived to establish the correlation between the stress and deterioration extent under temperature gradient load. After a numerical simulation that combines the thermal–structural interaction analysis and the vehicle–bridge interaction analysis, the strain range was proposed as an assessment index to ensure the stability and effectiveness of the evaluation results. Next, the Generalized Extreme Studentized Deviate method was used for detecting the outliers. The statistical results of the assessment index for different strengthening methods were compared to evaluate the associated strengthening efficiency, and the associated equivalent section height was calculated for visualizing the bridge condition after strengthening measures were taken. The results demonstrated that the proposed temperature-driven method was able to quantitatively evaluate the bridge strengthening effects with a high efficiency.
{"title":"A temperature-driven approach for quantitative assessment of strengthening effect of continuous bridges using structural health monitoring data","authors":"Xiaoyu Gong, Xiaodong Song, C. Cai, Guangqi Li, Wen Xiong","doi":"10.1177/14759217231181882","DOIUrl":"https://doi.org/10.1177/14759217231181882","url":null,"abstract":"The stiffness degeneration of small to medium span bridges has been increasingly observed in recent years, and it has become a major concern of government and bridge owners. A quantitative evaluation method for the bridge performance with strengthening measures is highly desired. Due to the advantages of uninterrupted traffic and long-term tracking capability, a temperature-driven approach for characterization of the correlation pattern between bridge temperature-induced strains and bridge status was proposed in the present study by using structural health monitoring data. First, a theoretical solution of the simplified bridge model was derived to establish the correlation between the stress and deterioration extent under temperature gradient load. After a numerical simulation that combines the thermal–structural interaction analysis and the vehicle–bridge interaction analysis, the strain range was proposed as an assessment index to ensure the stability and effectiveness of the evaluation results. Next, the Generalized Extreme Studentized Deviate method was used for detecting the outliers. The statistical results of the assessment index for different strengthening methods were compared to evaluate the associated strengthening efficiency, and the associated equivalent section height was calculated for visualizing the bridge condition after strengthening measures were taken. The results demonstrated that the proposed temperature-driven method was able to quantitatively evaluate the bridge strengthening effects with a high efficiency.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47269262","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 : 2023-07-01DOI: 10.1177/14759217221130132
Yue Hu, Yanping Zhu, F. Cui, Jing Xiao, Shuai Cao, Fucai Li, Wenjie Bao
The defect imaging based on guided wave provides an intuitive way for defect localization. Recently, sparse representation methods based on the damage sparsity assumption have been developed for defect imaging, where few sensors are used in these methods. However, these sparse imaging methods need repeatedly tuning the regularization parameter to obtain a good imaging performance. In this paper, an adaptive method based on complex Bayesian group Lasso is developed for localizing the damage. A group Lasso model is constructed to represent the defect imaging problem, and formulated by a sparse Bayesian learning (SBL) framework, where a hierarchical model of a Laplace prior is built to represent the group Lasso regularization. Estimations of the model variables are derived by using variational inference. In the proposed method, the model parameters are automatically updated without needing priori information. The effectiveness of the proposed method is verified by analyzing an experimental data.
{"title":"Complex Bayesian group Lasso for defect imaging with guided waves","authors":"Yue Hu, Yanping Zhu, F. Cui, Jing Xiao, Shuai Cao, Fucai Li, Wenjie Bao","doi":"10.1177/14759217221130132","DOIUrl":"https://doi.org/10.1177/14759217221130132","url":null,"abstract":"The defect imaging based on guided wave provides an intuitive way for defect localization. Recently, sparse representation methods based on the damage sparsity assumption have been developed for defect imaging, where few sensors are used in these methods. However, these sparse imaging methods need repeatedly tuning the regularization parameter to obtain a good imaging performance. In this paper, an adaptive method based on complex Bayesian group Lasso is developed for localizing the damage. A group Lasso model is constructed to represent the defect imaging problem, and formulated by a sparse Bayesian learning (SBL) framework, where a hierarchical model of a Laplace prior is built to represent the group Lasso regularization. Estimations of the model variables are derived by using variational inference. In the proposed method, the model parameters are automatically updated without needing priori information. The effectiveness of the proposed method is verified by analyzing an experimental data.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"22 1","pages":"2597 - 2608"},"PeriodicalIF":6.6,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42536249","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 : 2023-06-29DOI: 10.1177/14759217231173589
Hu Jiang, Yongying Jiang, J. Xiang
Renewable energy has increased in recent years with a consequential increase in equipment maintenance. Maintenance costs can be reduced by structural health monitoring techniques especially for wind turbine (WT) blade damages. However, the majority are not suitable for on-line measurements and quantitative detections. A quantitative damage detection method is developed to identify multiple damages in a WT blade under in-service operation conditions. Firstly, singular value decomposition is applied to reveal singular information in the operating deflection shape (ODS), which can be treated as damage locations. Secondly, whale optimization algorithm is utilized for a damage severity decision about the natural frequency database between damage severities and natural frequencies, which are constructed by finite element method (FEM) simulations on the detected damage locations in the WT blade. The procedure is applied to FEM numerical simulations of a single WT blade with two and three damages. By adding a certain noise to the simulation dataset, the robustness of the present method is validated. Furthermore, the laser scanning vibrometer is employed to test the ODS as well as natural frequencies of WT blades to testify the performance of the multiple damage detection method. Results show that the present method is effective for the detection of multi-damage in WT blades with a certain noise robustness.
{"title":"Method using singular value decomposition and whale optimization algorithm to quantitatively detect multiple damages in turbine blades","authors":"Hu Jiang, Yongying Jiang, J. Xiang","doi":"10.1177/14759217231173589","DOIUrl":"https://doi.org/10.1177/14759217231173589","url":null,"abstract":"Renewable energy has increased in recent years with a consequential increase in equipment maintenance. Maintenance costs can be reduced by structural health monitoring techniques especially for wind turbine (WT) blade damages. However, the majority are not suitable for on-line measurements and quantitative detections. A quantitative damage detection method is developed to identify multiple damages in a WT blade under in-service operation conditions. Firstly, singular value decomposition is applied to reveal singular information in the operating deflection shape (ODS), which can be treated as damage locations. Secondly, whale optimization algorithm is utilized for a damage severity decision about the natural frequency database between damage severities and natural frequencies, which are constructed by finite element method (FEM) simulations on the detected damage locations in the WT blade. The procedure is applied to FEM numerical simulations of a single WT blade with two and three damages. By adding a certain noise to the simulation dataset, the robustness of the present method is validated. Furthermore, the laser scanning vibrometer is employed to test the ODS as well as natural frequencies of WT blades to testify the performance of the multiple damage detection method. Results show that the present method is effective for the detection of multi-damage in WT blades with a certain noise robustness.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42236451","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}
Studying the crack propagation of ultrahigh-performance concrete (UHPC) helps us understand its mechanical mechanism and assess its structural performance. A novel method for crack separation and its characteristic evaluation was developed in this work. The proposed method introduces robust principal component analysis (RPCA) to decompose a data matrix from video streams stacked into a low-rank matrix and a sparse matrix, in which the sparse matrix represents the crack information. Compared with the cracks in a binary image, the obtained sparse matrix preserves rich crack information that can be used to quantify crack characteristics. The statistical characteristics of the crack area, the major and minor axes of the equivalent ellipse for crack regions, and the power spectral density are investigated and compared continuously. The proposed method is demonstrated by the crack development of UHPC under tensile loading. The analysis results indicate that RPCA can accurately separate cracks from the background. In the frequency domain by performing the Fourier transform of the sparse matrix, cracks are concentrated at small wavenumbers and the magnitude of small wavenumbers increases with an increase in the crack width. The relationship between the crack propagation and the stress–strain is also discussed. This work provides insight into the crack propagation of UHPC and an accumulated crack database for predicting the damage evolution of UHPC.
研究超高性能混凝土(UHPC)的裂缝扩展有助于我们了解其力学机理和评价其结构性能。本文提出了一种新的裂纹分离及其特性评价方法。该方法引入鲁棒主成分分析(robust principal component analysis, RPCA)，将视频流中的数据矩阵分解为低秩矩阵和稀疏矩阵，其中稀疏矩阵表示裂缝信息。与二值图像中的裂纹相比，得到的稀疏矩阵保留了丰富的裂纹信息，可用于量化裂纹特征。对裂纹区域的统计特征、裂纹区域等效椭圆的长、短轴以及功率谱密度进行了连续的研究和比较。以拉伸荷载作用下UHPC的裂纹发展为例验证了该方法的有效性。分析结果表明，RPCA可以准确地从背景中分离出裂纹。在频域中，通过对稀疏矩阵进行傅里叶变换，裂缝集中在小波数上，小波数的大小随着裂缝宽度的增加而增加。讨论了裂纹扩展与应力应变之间的关系。本研究为超高压混凝土的裂纹扩展提供了深入的认识，并为预测超高压混凝土的损伤演变提供了累积的裂纹数据库。
{"title":"Crack detection in ultrahigh-performance concrete using robust principal component analysis and characteristic evaluation in the frequency domain","authors":"Jixing Cao, Hai-jie He, Yao Zhang, Weigang Zhao, Zhi-guo Yan, Hehua Zhu","doi":"10.1177/14759217231178457","DOIUrl":"https://doi.org/10.1177/14759217231178457","url":null,"abstract":"Studying the crack propagation of ultrahigh-performance concrete (UHPC) helps us understand its mechanical mechanism and assess its structural performance. A novel method for crack separation and its characteristic evaluation was developed in this work. The proposed method introduces robust principal component analysis (RPCA) to decompose a data matrix from video streams stacked into a low-rank matrix and a sparse matrix, in which the sparse matrix represents the crack information. Compared with the cracks in a binary image, the obtained sparse matrix preserves rich crack information that can be used to quantify crack characteristics. The statistical characteristics of the crack area, the major and minor axes of the equivalent ellipse for crack regions, and the power spectral density are investigated and compared continuously. The proposed method is demonstrated by the crack development of UHPC under tensile loading. The analysis results indicate that RPCA can accurately separate cracks from the background. In the frequency domain by performing the Fourier transform of the sparse matrix, cracks are concentrated at small wavenumbers and the magnitude of small wavenumbers increases with an increase in the crack width. The relationship between the crack propagation and the stress–strain is also discussed. This work provides insight into the crack propagation of UHPC and an accumulated crack database for predicting the damage evolution of UHPC.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42087504","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 : 2023-06-22DOI: 10.1177/14759217231176958
Heng Zuo, H. Guo
In the structural health monitoring (SHM) of civil engineering, most of the structural damage is nonlinear damage, such as breathing cracks and bolt looseness. Under the excitation of external loads, the time-domain response data of the structure produced by these nonlinear damages have nonlinear features. In order to solve the time-domain nonlinear damage identification problem of complex structures, this paper proposes a nonlinear damage identification method based on the information distance of GNPAX/GARCH (general expression of system identification for linear and nonlinear with polynomial approximation and exogenous inputs/generalized autoregressive conditional heteroskedasticity) model. First, an order determination method based on Bayesian optimization to select the order of the GNPAX/GARCH model was proposed, and the GNPAX/GARCH model was established for damage identification. Then, the redundant structural items of GNPAX/GARCH model were removed by the model optimization method based on the structural pruning algorithm. Finally, the information distance of the GNPAX/GARCH model conditional heteroscedasticity series between the baseline state and test state was derived, and the structural damage source locations were determined according to the information distance. A three-story frame structure experiment and a stand structure experiment were used to verify the effectiveness of the proposed method. The results show that the proposed method can effectively identify the nonlinear damages caused by the component breathing crack and joint bolt looseness, verifying its robustness to the nonlinear damage identification of the multi-story and multi-span complex structures.
{"title":"Structural nonlinear damage identification based on the information distance of GNPAX/GARCH model and its experimental study","authors":"Heng Zuo, H. Guo","doi":"10.1177/14759217231176958","DOIUrl":"https://doi.org/10.1177/14759217231176958","url":null,"abstract":"In the structural health monitoring (SHM) of civil engineering, most of the structural damage is nonlinear damage, such as breathing cracks and bolt looseness. Under the excitation of external loads, the time-domain response data of the structure produced by these nonlinear damages have nonlinear features. In order to solve the time-domain nonlinear damage identification problem of complex structures, this paper proposes a nonlinear damage identification method based on the information distance of GNPAX/GARCH (general expression of system identification for linear and nonlinear with polynomial approximation and exogenous inputs/generalized autoregressive conditional heteroskedasticity) model. First, an order determination method based on Bayesian optimization to select the order of the GNPAX/GARCH model was proposed, and the GNPAX/GARCH model was established for damage identification. Then, the redundant structural items of GNPAX/GARCH model were removed by the model optimization method based on the structural pruning algorithm. Finally, the information distance of the GNPAX/GARCH model conditional heteroscedasticity series between the baseline state and test state was derived, and the structural damage source locations were determined according to the information distance. A three-story frame structure experiment and a stand structure experiment were used to verify the effectiveness of the proposed method. The results show that the proposed method can effectively identify the nonlinear damages caused by the component breathing crack and joint bolt looseness, verifying its robustness to the nonlinear damage identification of the multi-story and multi-span complex structures.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45991184","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 : 2023-06-20DOI: 10.1177/14759217231178453
Hong Zhang, Junfeng Xia, J. Zhou, L. Liao, Yangjian Xiao, Kai Tong, Senhua Zhang
Monitoring the prestress of prestressed concrete structures is hard but helpful. The resonance-enhanced magnetoelastic (REME) sensor could measure the stress of steel, but the measurement is influenced by the stress history. Thus, after analyzing the principle of the REME method according to the electric circuit theorem, the construction process and stress history of different prestress tendons were discussed. The prestress monitoring experiment showed that the induced voltage–prestress relationship was influenced by the stress history. To evaluate the prestress, a prestress evaluation method suitable for prestress steel strands was proposed. Using the precise calibration method, the monitoring errors in the construction stage and the prestress loss stage were less than 13.26% and 5.97%. The simplified calibration method reduced the workload of the calibration by 50% while the monitoring error increased by less than 4%. In addition, using the self-calibration method can avoid the influence of the differences in steel strands, leading to higher monitoring accuracy.
{"title":"Prestress monitoring for prestress tendons based on the resonance-enhanced magnetoelastic method considering the construction process","authors":"Hong Zhang, Junfeng Xia, J. Zhou, L. Liao, Yangjian Xiao, Kai Tong, Senhua Zhang","doi":"10.1177/14759217231178453","DOIUrl":"https://doi.org/10.1177/14759217231178453","url":null,"abstract":"Monitoring the prestress of prestressed concrete structures is hard but helpful. The resonance-enhanced magnetoelastic (REME) sensor could measure the stress of steel, but the measurement is influenced by the stress history. Thus, after analyzing the principle of the REME method according to the electric circuit theorem, the construction process and stress history of different prestress tendons were discussed. The prestress monitoring experiment showed that the induced voltage–prestress relationship was influenced by the stress history. To evaluate the prestress, a prestress evaluation method suitable for prestress steel strands was proposed. Using the precise calibration method, the monitoring errors in the construction stage and the prestress loss stage were less than 13.26% and 5.97%. The simplified calibration method reduced the workload of the calibration by 50% while the monitoring error increased by less than 4%. In addition, using the self-calibration method can avoid the influence of the differences in steel strands, leading to higher monitoring accuracy.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45917037","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 : 2023-06-16DOI: 10.1177/14759217231178652
Chunrong Hua, Libo Xiong, Lumei Lv, Dawei Dong, H. Ouyang
As it is difficult to distinguish multiple rotor faults with similar dynamic phenomena in noisy environments, a multi-fault classification method is proposed by combining the extracted trajectory phase feature, a parameter-optimized variational mode decomposition (VMD) method and a light gradient boosting machine (LightGBM) model. The trajectory phase feature is extracted from an axis trajectory by fusing the frequency, amplitude, and phase information related to rotor motion and can comprehensively describe the dynamic characteristics induced by different rotor faults. First, the vibration displacement signals in two orthogonal directions are collected to construct the axis trajectories with 12 rotor states including healthy, unbalance, misalignment, single crack, multiple cracks, and a mixture of them. Second, the trajectory phase feature is extracted from the vectorized axis trajectories, and the frequency spectra of trajectory phase angles under different rotor faults are analyzed through Fourier transform. Finally, a parameter-optimized VMD method combined with a LightGBM model is applied to classify multiple faults of rotor systems in different noisy environments based on the extracted trajectory phase feature. The 12 rotor states can be classified into nine categories based on the harmonic information of 1X–7X components (X is the rotating frequency of a rotor system) and other components with smaller amplitudes in the frequency spectra of trajectory phase angles. The average classification accuracy of the 12 rotor states exceeds 93.0%, and the recognition rate for each kind of fault is greater than 77.5% in noisy environments. The simulated and experimental results demonstrate the effectiveness and adaptability of the proposed multi-fault classification method. This work can provide a reference for the condition monitoring and fault diagnosis of rotor systems in engineering.
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