Héctor Triviño, Cisne Feijóo, Hugo Lugmania, Yolanda Vidal, Christian Tutiv'en
Early detection of damage in the support structure (submerged part) of an offshore wind turbine is crucial as it can help to prevent emergency shutdowns and extend the lifespan of the turbine. To this end, a promising proof-of-concept is stated, based on a transformer network, for the detection and localization of damage at the jacket-type support of an offshore wind turbine. To the best of the authors’ knowledge, this is the first time transformer-based models have been used for offshore wind turbine structural health monitoring. The proposed strategy employs a transformer-based framework for learning multivariate time series representation. The framework is based on the transformer architecture, which is a neural network architecture that has been shown to be highly effective for natural language processing tasks. A down-scaled laboratory model of an offshore wind turbine that simulates the different regions of operation of the wind turbine is employed to develop and validate the proposed methodology. The vibration signals collected from 8 accelerometers are used to analyze the dynamic behavior of the structure. The results obtained show a significant improvement compared to other approaches previously proposed in the literature. In particular, the stated methodology achieves an accuracy of 99.96% with an average training time of only 6.13 minutes due to the high parallelizability of the transformer network. In fact, as it is computationally highly efficient, it has the potential to be a useful tool for implementation in real-time monitoring systems.
{"title":"Damage Detection and Localization at the Jacket Support of an Offshore Wind Turbine Using Transformer Models","authors":"Héctor Triviño, Cisne Feijóo, Hugo Lugmania, Yolanda Vidal, Christian Tutiv'en","doi":"10.1155/2023/6646599","DOIUrl":"https://doi.org/10.1155/2023/6646599","url":null,"abstract":"Early detection of damage in the support structure (submerged part) of an offshore wind turbine is crucial as it can help to prevent emergency shutdowns and extend the lifespan of the turbine. To this end, a promising proof-of-concept is stated, based on a transformer network, for the detection and localization of damage at the jacket-type support of an offshore wind turbine. To the best of the authors’ knowledge, this is the first time transformer-based models have been used for offshore wind turbine structural health monitoring. The proposed strategy employs a transformer-based framework for learning multivariate time series representation. The framework is based on the transformer architecture, which is a neural network architecture that has been shown to be highly effective for natural language processing tasks. A down-scaled laboratory model of an offshore wind turbine that simulates the different regions of operation of the wind turbine is employed to develop and validate the proposed methodology. The vibration signals collected from 8 accelerometers are used to analyze the dynamic behavior of the structure. The results obtained show a significant improvement compared to other approaches previously proposed in the literature. In particular, the stated methodology achieves an accuracy of 99.96% with an average training time of only 6.13 minutes due to the high parallelizability of the transformer network. In fact, as it is computationally highly efficient, it has the potential to be a useful tool for implementation in real-time monitoring systems.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":" 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139139367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Stoura, V. Dertimanis, C. Hoelzl, Claudia Kossmann, Alfredo Cigada, Eleni Chatzi
According to the International Union of Railways, railway networks count more than one million kilometers of tracks worldwide, a number that is to rise further as the goal is to promote rail transportation as a sustainable means to face the challenge of increased mobility. However, such a vast expansion further necessitates efficient and reliable infrastructure monitoring schemes able to guarantee the quality and safety of rail transportation. Traditional monitoring approaches, relying on visual inspection and portable measuring devices, cannot rise to the task as they do not allow for continuous inspection of extended portions of rail infrastructure. Therefore, mobile monitoring methodologies based on dedicated diagnostic vehicles have emerged as an alternative. Despite revolutionizing traditional monitoring methods, such vehicles are usually expensive and can only operate under the suspension of regular rail service. In this work, we propose an alternative approach for mobile sensing of railway infrastructure based on on-board monitoring data collected from low-cost vibration sensors, e.g., accelerometers, which can be mounted on in-service trains. Specifically, we focus on identifying the roughness profile of the tracks and propose a fusion of reduced-order vehicle models with a Bayesian inference approach for joint input-state estimation. To enhance the inference, we opt for a prior updating of the vehicle model parameters on the basis of an unscented Kalman filter and available measurements from a diagnostic vehicle. The key contributions of this work are (i) the consideration of the dynamic interaction between trains and tracks, which is usually ignored in rail roughness estimation, (ii) the adoption of reduced train vehicle models that decrease the computational effort of the identification task, (iii) the updating of the vehicle parameters to account for inconsistencies in the model used, and (iv) the application of the proposed methodology to actual acceleration measurements collected from a diagnostic vehicle of the Swiss Federal Railways network.
{"title":"A Model-Based Bayesian Inference Approach for On-Board Monitoring of Rail Roughness Profiles: Application on Field Measurement Data of the Swiss Federal Railways Network","authors":"C. Stoura, V. Dertimanis, C. Hoelzl, Claudia Kossmann, Alfredo Cigada, Eleni Chatzi","doi":"10.1155/2023/8855542","DOIUrl":"https://doi.org/10.1155/2023/8855542","url":null,"abstract":"According to the International Union of Railways, railway networks count more than one million kilometers of tracks worldwide, a number that is to rise further as the goal is to promote rail transportation as a sustainable means to face the challenge of increased mobility. However, such a vast expansion further necessitates efficient and reliable infrastructure monitoring schemes able to guarantee the quality and safety of rail transportation. Traditional monitoring approaches, relying on visual inspection and portable measuring devices, cannot rise to the task as they do not allow for continuous inspection of extended portions of rail infrastructure. Therefore, mobile monitoring methodologies based on dedicated diagnostic vehicles have emerged as an alternative. Despite revolutionizing traditional monitoring methods, such vehicles are usually expensive and can only operate under the suspension of regular rail service. In this work, we propose an alternative approach for mobile sensing of railway infrastructure based on on-board monitoring data collected from low-cost vibration sensors, e.g., accelerometers, which can be mounted on in-service trains. Specifically, we focus on identifying the roughness profile of the tracks and propose a fusion of reduced-order vehicle models with a Bayesian inference approach for joint input-state estimation. To enhance the inference, we opt for a prior updating of the vehicle model parameters on the basis of an unscented Kalman filter and available measurements from a diagnostic vehicle. The key contributions of this work are (i) the consideration of the dynamic interaction between trains and tracks, which is usually ignored in rail roughness estimation, (ii) the adoption of reduced train vehicle models that decrease the computational effort of the identification task, (iii) the updating of the vehicle parameters to account for inconsistencies in the model used, and (iv) the application of the proposed methodology to actual acceleration measurements collected from a diagnostic vehicle of the Swiss Federal Railways network.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"94 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139145713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Structural health monitoring (SHM) systems are widely deployed to monitor the dynamic behaviors of large civil infrastructures such as bridges and tall buildings. Global Navigation Satellite System- (GNSS-) based technologies are often a key component in such an SHM system considering the unique capability of GNSS in determining real-time displacements. GNSS often integrates with an accelerometer to achieve complementary advantages. However, due to the various error sources in GNSS measurements and accelerometer, accuracies of GNSS and accelerometer fusion results often cannot meet the requirements of SHM. We propose to integrate a multi-antenna GNSS and an accelerometer with an unscented multi-rate Kalman filter (UMRKF-MA) to correct the system misalignment errors between the sensors, aiming to produce a much more accurate real-time displacement measurement technology for monitoring large civil infrastructures. Extensive experiments with datasets gathered using a shaking table have indicated that the proposed method was able to improve the accuracy of real-time displacement measurements by up to about 40–65% compared to some existing approaches, and that a 1 mm level of real-time monitoring of displacements could be achieved with the method. The method has also been applied to process a dataset from a real-world long-span bridge when heavy vehicles passed through the bridge in a loading test and significantly improved results were obtained.
{"title":"Correction of Misalignment Errors in the Integrated GNSS and Accelerometer System for Structural Displacement Monitoring","authors":"Xuanyu Qu, Xiaoli Ding, You-Lin Xu, Wenkun Yu","doi":"10.1155/2023/4919151","DOIUrl":"https://doi.org/10.1155/2023/4919151","url":null,"abstract":"Structural health monitoring (SHM) systems are widely deployed to monitor the dynamic behaviors of large civil infrastructures such as bridges and tall buildings. Global Navigation Satellite System- (GNSS-) based technologies are often a key component in such an SHM system considering the unique capability of GNSS in determining real-time displacements. GNSS often integrates with an accelerometer to achieve complementary advantages. However, due to the various error sources in GNSS measurements and accelerometer, accuracies of GNSS and accelerometer fusion results often cannot meet the requirements of SHM. We propose to integrate a multi-antenna GNSS and an accelerometer with an unscented multi-rate Kalman filter (UMRKF-MA) to correct the system misalignment errors between the sensors, aiming to produce a much more accurate real-time displacement measurement technology for monitoring large civil infrastructures. Extensive experiments with datasets gathered using a shaking table have indicated that the proposed method was able to improve the accuracy of real-time displacement measurements by up to about 40–65% compared to some existing approaches, and that a 1 mm level of real-time monitoring of displacements could be achieved with the method. The method has also been applied to process a dataset from a real-world long-span bridge when heavy vehicles passed through the bridge in a loading test and significantly improved results were obtained.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138961853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To mitigate in-plane vibrations of wind turbine blades, a track tuned mass damper (TMD) is proposed and its performance for mitigating blade in-plane vibration is investigated considering various influence factors. Firstly, the organization and operational principles of the damping control device are explained. Then, the equations of motion of the individual TMD-equipped blade are then deduced from Euler–Lagrange. Secondly, blade’s wind loading is calculated by blade element momentum theory considering the blade rotation effect through the rotating sample spectrum. Thirdly, the dynamical response of the blade based on the MATLAB/SIMULINK tool is calculated. The peak maximum displacement and standard deviation of the blade tip are chosen as the estimation indicators to assess the TMD’s effectiveness of the device considering actually various argument including mass ratio μ , damping ratio ξ , and installation position x 0 / L . Based on the assumption that the mass block in the vibration reduction control device has no contact with the inside surface of the blade web in operation, the optimal relative values of mass ratio, damping ratio, and installation position of a single blade are determined as 0.03, 15%, and 0.55, respectively. As a result, the reduction of the peak value and the standard deviation can reach 52.78% and 53.75%, respectively. Therefore, with the optimal parameters, the designed vibration control device effectively not only reduces the blade tip displacement but also avoids the damage due to in-plane vibrations.
{"title":"Mitigation of In-Plane Vibrations in Large-Scale Wind Turbine Blades with a Track Tuned Mass Damper","authors":"Wanrun Li, Shuanbao Yan, Ganggang Li, Yongfeng Du","doi":"10.1155/2023/8645831","DOIUrl":"https://doi.org/10.1155/2023/8645831","url":null,"abstract":"To mitigate in-plane vibrations of wind turbine blades, a track tuned mass damper (TMD) is proposed and its performance for mitigating blade in-plane vibration is investigated considering various influence factors. Firstly, the organization and operational principles of the damping control device are explained. Then, the equations of motion of the individual TMD-equipped blade are then deduced from Euler–Lagrange. Secondly, blade’s wind loading is calculated by blade element momentum theory considering the blade rotation effect through the rotating sample spectrum. Thirdly, the dynamical response of the blade based on the MATLAB/SIMULINK tool is calculated. The peak maximum displacement and standard deviation of the blade tip are chosen as the estimation indicators to assess the TMD’s effectiveness of the device considering actually various argument including mass ratio μ , damping ratio ξ , and installation position x 0 / L . Based on the assumption that the mass block in the vibration reduction control device has no contact with the inside surface of the blade web in operation, the optimal relative values of mass ratio, damping ratio, and installation position of a single blade are determined as 0.03, 15%, and 0.55, respectively. As a result, the reduction of the peak value and the standard deviation can reach 52.78% and 53.75%, respectively. Therefore, with the optimal parameters, the designed vibration control device effectively not only reduces the blade tip displacement but also avoids the damage due to in-plane vibrations.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"38 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139175296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. J. Jiang, Y. L. Xu, J. Zhu, G. Q. Zhang, D. H. Dan
Vortex-induced force (VIF) identification and modelling of a long-span bridge are often conducted in terms of aeroelastic sectional model tests in wind tunnels. However, there are uncertainties inherent in wind tunnel model tests so that vortex-induced vibration (VIV) still occurs in real long-span bridges designed according to wind tunnel test results. This paper presents a framework for VIF identification of a long-span bridge based on field-measured wind and acceleration data. The framework is composed of the four steps: (1) decompose field-measured acceleration response time histories using variational mode decomposition (VMD) method; (2) obtain velocity and displacement response time histories using frequency domain integration (FDI) method; (3) establish and update the finite element model and identify the generalized VIF time histories of the bridge; and (4) identify the parameters in the polynomial VIF models and decide the most suitable VIF model. The proposed framework is finally applied to a real suspension bridge with a recent VIV event. The results show that the proposed framework can accurately identify the generalized VIF acting on the bridge from the field-measured acceleration and wind data, and the derived most suitable VIF model can produce almost the same vortex-induced response (VIR) as the measured ones.
{"title":"Vortex-Induced Force Identification of a Long-Span Bridge Based on Field Measurement Data","authors":"S. J. Jiang, Y. L. Xu, J. Zhu, G. Q. Zhang, D. H. Dan","doi":"10.1155/2023/9361196","DOIUrl":"https://doi.org/10.1155/2023/9361196","url":null,"abstract":"Vortex-induced force (VIF) identification and modelling of a long-span bridge are often conducted in terms of aeroelastic sectional model tests in wind tunnels. However, there are uncertainties inherent in wind tunnel model tests so that vortex-induced vibration (VIV) still occurs in real long-span bridges designed according to wind tunnel test results. This paper presents a framework for VIF identification of a long-span bridge based on field-measured wind and acceleration data. The framework is composed of the four steps: (1) decompose field-measured acceleration response time histories using variational mode decomposition (VMD) method; (2) obtain velocity and displacement response time histories using frequency domain integration (FDI) method; (3) establish and update the finite element model and identify the generalized VIF time histories of the bridge; and (4) identify the parameters in the polynomial VIF models and decide the most suitable VIF model. The proposed framework is finally applied to a real suspension bridge with a recent VIV event. The results show that the proposed framework can accurately identify the generalized VIF acting on the bridge from the field-measured acceleration and wind data, and the derived most suitable VIF model can produce almost the same vortex-induced response (VIR) as the measured ones.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"239 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138997105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The integration of structural identification and vibration optimal control has been studied. Since the semiactive optimization vibrational control of civil structures needs to be implemented by massive control devices such as mass dampers, it is necessary to investigate the real-time integration of identification and semiactive optimization vibration control for mass damper-building combined systems. However, there is a lack of such studies in the literature. In this paper, a methodology is presented for real-time integration of identification and semiactive optimization vibration control of the mass damper-building combined system under known/unknown seismic excitations. For the combined system under known seismic excitations, the identification is implemented by the extended Kalman filter (EKF) using only partial structural acceleration responses. The identified structural state and parameters of mass damper-building systems are integrated in real time for the optimal control of systems by the linear-quadratic regulator (LQR) control algorithm and the Hrovat semiactive optimization control strategy via semiactive optimization mass dampers (SAMD). Then, it is extended to the scenario of unknown seismic excitations. The partially measured structural acceleration responses are absolute ones in this case, so the generalized extended Kalman filter with unknown input (GEKF-UI) developed by the authors is used to identify the structural input-state parameters of the mass dampers-building combined systems. The identification results are also integrated in real time for the semiactive optimization control of the combined system via SAMD. Two numerical simulation examples are used to test the proposed integration methods. It is shown that the proposed integration methods can reach almost the same optimal control effects as the conventional semiactive optimization control with known parameters of the mass damper-building combined systems under known/unknown seismic excitations.
{"title":"Real-Time Integration of Identification and Semiactive Optimization Control for Mass Damper-Building Combined Systems under Known/Unknown Seismic Excitations","authors":"Chang Yin, Jubin Lu, Chunyan Xiang, Ying Lei","doi":"10.1155/2023/6658364","DOIUrl":"https://doi.org/10.1155/2023/6658364","url":null,"abstract":"The integration of structural identification and vibration optimal control has been studied. Since the semiactive optimization vibrational control of civil structures needs to be implemented by massive control devices such as mass dampers, it is necessary to investigate the real-time integration of identification and semiactive optimization vibration control for mass damper-building combined systems. However, there is a lack of such studies in the literature. In this paper, a methodology is presented for real-time integration of identification and semiactive optimization vibration control of the mass damper-building combined system under known/unknown seismic excitations. For the combined system under known seismic excitations, the identification is implemented by the extended Kalman filter (EKF) using only partial structural acceleration responses. The identified structural state and parameters of mass damper-building systems are integrated in real time for the optimal control of systems by the linear-quadratic regulator (LQR) control algorithm and the Hrovat semiactive optimization control strategy via semiactive optimization mass dampers (SAMD). Then, it is extended to the scenario of unknown seismic excitations. The partially measured structural acceleration responses are absolute ones in this case, so the generalized extended Kalman filter with unknown input (GEKF-UI) developed by the authors is used to identify the structural input-state parameters of the mass dampers-building combined systems. The identification results are also integrated in real time for the semiactive optimization control of the combined system via SAMD. Two numerical simulation examples are used to test the proposed integration methods. It is shown that the proposed integration methods can reach almost the same optimal control effects as the conventional semiactive optimization control with known parameters of the mass damper-building combined systems under known/unknown seismic excitations.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"8 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138997948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thomas Maetz, Jonas Kappel, M. Wiemann, Dirk Bergmannshoff, Manfred Hägelen, R. Jetten, Matthias Schmidt, Johannes Käsgen, Marco Jackel, J. Moll, Peter Kraemer, Viktor Krozer
Offshore wind turbines play a significant role in the expansion of clean and renewable energy. However, their exposure to harsh marine environments and dynamic loading conditions poses significant challenges to their structural integrity. In particular, the grouted connection, serving as the crucial interface between the monopile and the transition piece, is susceptible to cracking and particle washout that can lead to destabilizing grout erosion over time. In this paper, we propose a microwave structural health monitoring (SHM) approach for damage detection in grouted connections based on a stepped-frequency continuous wave radar. The methodology exploits ultra-wideband (UWB) electromagnetic wave propagation in the frequency range from 100 MHz to 2 GHz, where the microwaves propagate along the concrete-type dielectric material guided by the surrounding steel cylinders. For the proof of concept, a scaled laboratory demonstrator was built that realistically models the dynamic loading experienced by a full-scale monopile. The structure was equipped with an UWB radar system using two transmitting and three receiving antennas directly coupled to the grout. For validation, a large number of other sensors, i.e., accelerometers, strain gauges, and acoustic emission sensors have also been installed and measured synchronously during the fatigue test. It is demonstrated here that the proposed SHM methodology offers a nondestructive and real-time method for assessing the structural integrity of the grouted connection directly, actively, and automatically. This has the potential to support predictive maintenance activities in the future.
{"title":"Microwave Structural Health Monitoring of the Grouted Connection of a Monopile-Based Offshore Wind Turbine: Fatigue Testing Using a Scaled Laboratory Demonstrator","authors":"Thomas Maetz, Jonas Kappel, M. Wiemann, Dirk Bergmannshoff, Manfred Hägelen, R. Jetten, Matthias Schmidt, Johannes Käsgen, Marco Jackel, J. Moll, Peter Kraemer, Viktor Krozer","doi":"10.1155/2023/1981892","DOIUrl":"https://doi.org/10.1155/2023/1981892","url":null,"abstract":"Offshore wind turbines play a significant role in the expansion of clean and renewable energy. However, their exposure to harsh marine environments and dynamic loading conditions poses significant challenges to their structural integrity. In particular, the grouted connection, serving as the crucial interface between the monopile and the transition piece, is susceptible to cracking and particle washout that can lead to destabilizing grout erosion over time. In this paper, we propose a microwave structural health monitoring (SHM) approach for damage detection in grouted connections based on a stepped-frequency continuous wave radar. The methodology exploits ultra-wideband (UWB) electromagnetic wave propagation in the frequency range from 100 MHz to 2 GHz, where the microwaves propagate along the concrete-type dielectric material guided by the surrounding steel cylinders. For the proof of concept, a scaled laboratory demonstrator was built that realistically models the dynamic loading experienced by a full-scale monopile. The structure was equipped with an UWB radar system using two transmitting and three receiving antennas directly coupled to the grout. For validation, a large number of other sensors, i.e., accelerometers, strain gauges, and acoustic emission sensors have also been installed and measured synchronously during the fatigue test. It is demonstrated here that the proposed SHM methodology offers a nondestructive and real-time method for assessing the structural integrity of the grouted connection directly, actively, and automatically. This has the potential to support predictive maintenance activities in the future.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"36 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139006559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jooyoung Park, Wonkyu Kim, Kyo-Young Jeon, Seunghee Park
Continuous monitoring of the prestressed members of a bridge under construction using the free cantilever method (FCM) is crucial for ensuring bridge safety. Temperature-sensitive sensors require special considerations as they may misinterpret the signal and tension. Moreover, the unnecessary and inappropriate use of features obtained from the sensor signal can deteriorate the efficiency of the signal and, therefore, tension analysis. This study proposes a tension estimation method using an embedded elastomagnetic (EM) sensor with a temperature-compensation technique. Changes in the signal due to the tension in the temporary steel rods were analyzed using a full-scale test, and the sensor data were acquired for 15 months via the field application. The temperature effect on the signal could be removed by subtracting the tension from the signal using the thermistor data, reducing the error by 91.99% when considering permeability. Additionally, linear regression (LR) and machine learning (ML) algorithms were adopted to predict the tension. Furthermore, the performances of both algorithms were compared using mean absolute error (MAE) and R2. For the prediction using each feature in magnetic hysteresis, LR surpassed ML and the permeability exhibited the highest prediction performance. Meanwhile, predictions using multiple features were attempted to investigate the applicability of ML. Two cases of prediction were performed using ML: on using all the features and the other using three features excluding coercivity, which showed poor relevance to tension. As a result, the performance of the tension prediction was improved significantly compared to the results obtained by LR. In summary, the obtained results have demonstrated that the utilization of selective features of data with temperature compensation techniques could enhance predictive power.
使用自由悬臂法（FCM）对在建桥梁的预应力构件进行连续监测，对于确保桥梁安全至关重要。对温度敏感的传感器可能会误读信号和张力，因此需要特别考虑。此外，不必要和不适当地使用从传感器信号中获得的特征会降低信号的效率，从而影响张力分析。本研究提出了一种使用嵌入式弹性电磁（EM）传感器和温度补偿技术的张力估算方法。通过全尺寸试验分析了临时钢棒张力引起的信号变化，并通过现场应用获取了传感器 15 个月的数据。通过使用热敏电阻数据从信号中减去拉力，可以消除温度对信号的影响，在考虑渗透率的情况下，误差减少了 91.99%。此外，还采用了线性回归（LR）和机器学习（ML）算法来预测张力。此外，还使用平均绝对误差（MAE）和 R2 比较了两种算法的性能。在使用磁滞中的每个特征进行预测时，LR 超过了 ML，磁导率的预测性能最高。同时，为了研究 ML 的适用性，尝试了使用多个特征进行预测。使用 ML 进行了两种预测：一种是使用所有特征，另一种是使用除矫顽力之外的三个特征，后者与张力的相关性较差。因此，与 LR 预测结果相比，张力预测的性能有了显著提高。总之，所获得的结果表明，利用温度补偿技术选择性地使用数据特征可以提高预测能力。
{"title":"Elastomagnetic Sensor-Based Long-Term Tension Monitoring of Prestressed Bridge Member with Temperature Compensation","authors":"Jooyoung Park, Wonkyu Kim, Kyo-Young Jeon, Seunghee Park","doi":"10.1155/2023/5316136","DOIUrl":"https://doi.org/10.1155/2023/5316136","url":null,"abstract":"Continuous monitoring of the prestressed members of a bridge under construction using the free cantilever method (FCM) is crucial for ensuring bridge safety. Temperature-sensitive sensors require special considerations as they may misinterpret the signal and tension. Moreover, the unnecessary and inappropriate use of features obtained from the sensor signal can deteriorate the efficiency of the signal and, therefore, tension analysis. This study proposes a tension estimation method using an embedded elastomagnetic (EM) sensor with a temperature-compensation technique. Changes in the signal due to the tension in the temporary steel rods were analyzed using a full-scale test, and the sensor data were acquired for 15 months via the field application. The temperature effect on the signal could be removed by subtracting the tension from the signal using the thermistor data, reducing the error by 91.99% when considering permeability. Additionally, linear regression (LR) and machine learning (ML) algorithms were adopted to predict the tension. Furthermore, the performances of both algorithms were compared using mean absolute error (MAE) and R2. For the prediction using each feature in magnetic hysteresis, LR surpassed ML and the permeability exhibited the highest prediction performance. Meanwhile, predictions using multiple features were attempted to investigate the applicability of ML. Two cases of prediction were performed using ML: on using all the features and the other using three features excluding coercivity, which showed poor relevance to tension. As a result, the performance of the tension prediction was improved significantly compared to the results obtained by LR. In summary, the obtained results have demonstrated that the utilization of selective features of data with temperature compensation techniques could enhance predictive power.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"21 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139010644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
X. Ning, Wei Huang, Guoshan Xu, Zhen Wang, Bin Wu, Lichang Zheng, Bin Xu
Adaptive control methods have been widely adopted to handle the variable time delay in real-time hybrid simulation (RTHS). Nevertheless, the initial parameter settings in adaptive control law, the parameter estimation method, and the testing system nonlinearity will affect RTHS’s accuracy and stability at different levels. To this end, this study proposes a novel model-based adaptive feedforward-feedback control method that considers an additive error model. In the proposed method, the time delay and amplitude discrepancy are roughly compensated by a feedforward controller and then finely reduced by an adaptive controller, and an outer-loop control formed by the feedback controller is introduced to improve the ability and robustness furthermore. What’s more, the testing system, composed of the transfer system and physical specimen, is divided into the nominal and additive error models. The feedforward controller is devised using the inverse nominal model, whose parameters are constant. The adaptive controller is designed to adopt a discrete-time additive error model, in which the parameters are identified online by the Kalman filter. Numerical simulations, parametric studies, and actual experiments were carried out to inspect the feasibility and effectiveness of this method thoroughly. Results indicate that the proposed method can effectively improve the accuracy and stability of RTHS and significantly reduce the dependence on the adaptive control law. Moreover, the proposed method exhibits strong robustness and is, therefore, useful in RTHS.
{"title":"A Novel Model-Based Adaptive Feedforward-Feedback Control Method for Real-Time Hybrid Simulation considering Additive Error Model","authors":"X. Ning, Wei Huang, Guoshan Xu, Zhen Wang, Bin Wu, Lichang Zheng, Bin Xu","doi":"10.1155/2023/5550580","DOIUrl":"https://doi.org/10.1155/2023/5550580","url":null,"abstract":"Adaptive control methods have been widely adopted to handle the variable time delay in real-time hybrid simulation (RTHS). Nevertheless, the initial parameter settings in adaptive control law, the parameter estimation method, and the testing system nonlinearity will affect RTHS’s accuracy and stability at different levels. To this end, this study proposes a novel model-based adaptive feedforward-feedback control method that considers an additive error model. In the proposed method, the time delay and amplitude discrepancy are roughly compensated by a feedforward controller and then finely reduced by an adaptive controller, and an outer-loop control formed by the feedback controller is introduced to improve the ability and robustness furthermore. What’s more, the testing system, composed of the transfer system and physical specimen, is divided into the nominal and additive error models. The feedforward controller is devised using the inverse nominal model, whose parameters are constant. The adaptive controller is designed to adopt a discrete-time additive error model, in which the parameters are identified online by the Kalman filter. Numerical simulations, parametric studies, and actual experiments were carried out to inspect the feasibility and effectiveness of this method thoroughly. Results indicate that the proposed method can effectively improve the accuracy and stability of RTHS and significantly reduce the dependence on the adaptive control law. Moreover, the proposed method exhibits strong robustness and is, therefore, useful in RTHS.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138597238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
During the operation of commercial vehicles, drivers are usually exposed to long-term vibrations and acquire several health problems. Moreover, the end-stop impacts caused by large-magnitude vibrations or shocks may affect driving performance and result in injuries. A study of magnetorheological (MR) seat suspension controlled by a novel tuning control strategy is conducted in this research to reduce vibrations and avoid end-stop impacts. First, the MR damper’s characteristics are tested, and a mathematical model of MR seat suspension is established. Then, an improved tuning control strategy is designed based on this model. The proposed strategy has three control stages that can be adjusted according to the suspension stroke to improve seat comfort or avoid end-stop impacts. Each part of the control strategy is designed separately, and the vibration attenuation performance of this seat suspension is evaluated with a simulation for three excitations, i.e., harmonic excitation, bump excitation, and random road excitation. Finally, an experiment is conducted to verify the conclusion of the simulation. The seat suspension with the proposed control shows good performances on vibration attenuation and end-stop impact reduction. Compared with a passive seat, the vibration level is reduced by around 27% and end-stop impact is avoided when semiactive suspension with the proposed strategy is used. It also shows the best overall performance among the three experimental algorithms. Both the simulation and the experiment results indicate that the vibration attenuation performance of the seat suspension can be greatly improved with the improved tuning control strategy.
{"title":"A Novel Application of Magnetorheological Seat Suspension with an Improved Tuning Control Strategy","authors":"Yuxuan Liang, Xiaomin Dong, W. Ao, Yi-Qing Ni","doi":"10.1155/2023/3985363","DOIUrl":"https://doi.org/10.1155/2023/3985363","url":null,"abstract":"During the operation of commercial vehicles, drivers are usually exposed to long-term vibrations and acquire several health problems. Moreover, the end-stop impacts caused by large-magnitude vibrations or shocks may affect driving performance and result in injuries. A study of magnetorheological (MR) seat suspension controlled by a novel tuning control strategy is conducted in this research to reduce vibrations and avoid end-stop impacts. First, the MR damper’s characteristics are tested, and a mathematical model of MR seat suspension is established. Then, an improved tuning control strategy is designed based on this model. The proposed strategy has three control stages that can be adjusted according to the suspension stroke to improve seat comfort or avoid end-stop impacts. Each part of the control strategy is designed separately, and the vibration attenuation performance of this seat suspension is evaluated with a simulation for three excitations, i.e., harmonic excitation, bump excitation, and random road excitation. Finally, an experiment is conducted to verify the conclusion of the simulation. The seat suspension with the proposed control shows good performances on vibration attenuation and end-stop impact reduction. Compared with a passive seat, the vibration level is reduced by around 27% and end-stop impact is avoided when semiactive suspension with the proposed strategy is used. It also shows the best overall performance among the three experimental algorithms. Both the simulation and the experiment results indicate that the vibration attenuation performance of the seat suspension can be greatly improved with the improved tuning control strategy.","PeriodicalId":22049,"journal":{"name":"Structural Control and Health Monitoring","volume":"563 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139243259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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