<div style="text-align:justify;"> Load identification method is one of the major technical difficulties of non-intrusive composite monitoring. Binary V-I trajectory image can reflect the original V-I t...<div style="text-align:justify;"> Load identification method is one of the major technical difficulties of non-intrusive composite monitoring. Binary V-I trajectory image can reflect the original V-I trajectory characteristics to a large extent, so it is widely used in load identification. However, using single binary V-I trajectory feature for load identification has certain limitations. In order to improve the accuracy of load identification, the power feature is added on the basis of the binary V-I trajectory feature in this paper. We change the initial binary V-I trajectory into a new 3D feature by mapping the power feature to the third dimension. In order to reduce the impact of imbalance samples on load identification, the SVM SMOTE algorithm is used to balance the samples. Based on the deep learning method, the convolutional neural network model is used to extract the newly produced 3D feature to achieve load identification in this paper. The results indicate the new 3D feature has better observability and the proposed model has higher identification performance compared with other classification models on the public data set PLAID. </div>展开更多
The identification of the traction acting on a portion of the surface of an anisotropic solid is very important in structural health monitoring and optimal design of structures. The traction can be determined using in...The identification of the traction acting on a portion of the surface of an anisotropic solid is very important in structural health monitoring and optimal design of structures. The traction can be determined using inverse methods in which displacement or strain measurements are taken at several points on the body. This paper presents an inverse method based on the method of fundamental solutions for the traction identification problem in two-dimensional anisotropic elasticity. The method of fundamental solutions is an efficient boundary-type meshless method widely used for analyzing various problems. Since the problem is linear, the sensitivity analysis is simply performed by solving the corresponding direct problem several times with different loads. The effects of important parameters such as the number of measurement data, the position of the measurement points, the amount of measurement error, and the type of measurement, i.e., displacement or strain, on the results are also investigated. The results obtained show that the presented inverse method is suitable for the problem of traction identification. It can be concluded from the results that the use of strain measurements in the inverse analysis leads to more accurate results than the use of displacement measurements. It is also found that measurement points closer to the boundary with unknown traction provide more reliable solutions. Additionally, it is found that increasing the number of measurement points increases the accuracy of the inverse solution. However, in cases with a large number of measurement points, further increasing the number of measurement data has little effect on the results.展开更多
Modern warfare demands weapons capable of penetrating substantial structures,which presents sig-nificant challenges to the reliability of the electronic devices that are crucial to the weapon's perfor-mance.Due to...Modern warfare demands weapons capable of penetrating substantial structures,which presents sig-nificant challenges to the reliability of the electronic devices that are crucial to the weapon's perfor-mance.Due to miniaturization of electronic components,it is challenging to directly measure or numerically predict the mechanical response of small-sized critical interconnections in board-level packaging structures to ensure the mechanical reliability of electronic devices in projectiles under harsh working conditions.To address this issue,an indirect measurement method using the Bayesian regularization-based load identification was proposed in this study based on finite element(FE)pre-dictions to estimate the load applied on critical interconnections of board-level packaging structures during the process of projectile penetration.For predicting the high-strain-rate penetration process,an FE model was established with elasto-plastic constitutive models of the representative packaging ma-terials(that is,solder material and epoxy molding compound)in which material constitutive parameters were calibrated against the experimental results by using the split-Hopkinson pressure bar.As the impact-induced dynamic bending of the printed circuit board resulted in an alternating tensile-compressive loading on the solder joints during penetration,the corner solder joints in the edge re-gions experience the highest S11 and strain,making them more prone to failure.Based on FE predictions at different structural scales,an improved Bayesian method based on augmented Tikhonov regulariza-tion was theoretically proposed to address the issues of ill-posed matrix inversion and noise sensitivity in the load identification at the critical solder joints.By incorporating a wavelet thresholding technique,the method resolves the problem of poor load identification accuracy at high noise levels.The proposed method achieves satisfactorily small relative errors and high correlation coefficients in identifying the mechanical response of local interconnections in board-level packaging structures,while significantly balancing the smoothness of response curves with the accuracy of peak identification.At medium and low noise levels,the relative error is less than 6%,while it is less than 10%at high noise levels.The proposed method provides an effective indirect approach for the boundary conditions of localized solder joints during the projectile penetration process,and its philosophy can be readily extended to other scenarios of multiscale analysis for highly nonlinear materials and structures under extreme loading conditions.展开更多
The determination of the dynamic load is one of the indispensable technologies for structure design and health monitoring for aerospace vehicles.However,it is a significant challenge to measure the external excitation...The determination of the dynamic load is one of the indispensable technologies for structure design and health monitoring for aerospace vehicles.However,it is a significant challenge to measure the external excitation directly.By contrast,the technique of dynamic load identification based on the dynamic model and the response information is a feasible access to obtain the dynamic load indirectly.Furthermore,there are multi-source uncertainties which cannot be neglected for complex systems in the load identification process,especially for aerospace vehicles.In this paper,recent developments in the dynamic load identification field for aerospace vehicles considering multi-source uncertainties are reviewed,including the deterministic dynamic load identification and uncertain dynamic load identification.The inversion methods with different principles of concentrated and distributed loads,and the quantification and propagation analysis for multi-source uncertainties are discussed.Eventually,several possibilities remaining to be explored are illustrated in brief.展开更多
Based on the platform of Matlab and the theory of digital signal processing, we propose a method in the cepstrum domain for dynamic load spectra identification of machinery. We demonstrate that the dynamic load spectr...Based on the platform of Matlab and the theory of digital signal processing, we propose a method in the cepstrum domain for dynamic load spectra identification of machinery. We demonstrate that the dynamic load spectra can be identified from the response signal of the system, based on cepstra. An ARMA model is built based on the harmonic retrieval by high-order spectra. The coefficients of a Green function are determined and the window width can be estimated. Finally the effectiveness of the method is validated by simulation results.展开更多
We introduce the extended Kalman filter(EKF)method combined with the least square estimation to identify the unknown load acting on the time-varying structure and realize the tracking of the structural parameters of t...We introduce the extended Kalman filter(EKF)method combined with the least square estimation to identify the unknown load acting on the time-varying structure and realize the tracking of the structural parameters of the time-varying system.Firstly,we propose the dynamic load identification method when the unknown parameters are stiffness coefficients.Then,a five-degree-of-freedom slowly-varying-stiffness structure is introduced to verify the effectiveness and the accuracy of the EKF method.The results show that the EKF method can accurately identify unknown loads and structural parameters simultaneously even considering noises in the input data.展开更多
For practical engineering structures,it is usually difficult to measure external load distribution in a direct manner,which makes inverse load identification important.Specifically,load identification is a typical inv...For practical engineering structures,it is usually difficult to measure external load distribution in a direct manner,which makes inverse load identification important.Specifically,load identification is a typical inverse problem,for which the models(e.g.,response matrix)are often ill-posed,resulting in degraded accuracy and impaired noise immunity of load identification.This study aims at identifying external loads in a stiffened plate structure,through comparing the effectiveness of different methods for parameter selection in regulation problems,including the Generalized Cross Validation(GCV)method,the Ordinary Cross Validation method and the truncated singular value decomposition method.With demonstrated high accuracy,the GCV method is used to identify concentrated loads in three different directions(e.g.,vertical,lateral and longitudinal)exerted on a stiffened plate.The results show that the GCV method is able to effectively identify multi-source static loads,with relative errors less than 5%.Moreover,under the situation of swept frequency excitation,when the excitation frequency is near the natural frequency of the structure,the GCV method can achieve much higher accuracy compared with direct inversion.At other excitation frequencies,the average recognition error of the GCV method load identification less than 10%.展开更多
A dynamic load identification model of structural system based on the gener-alized orthogonal polynomial theory is provided, and the least Square discrete algorithm foridentifying the dynamic load is supplied. The mai...A dynamic load identification model of structural system based on the gener-alized orthogonal polynomial theory is provided, and the least Square discrete algorithm foridentifying the dynamic load is supplied. The main key is that the convolution relationsbetween the input and output of the system in time domain are transformed into linear oP-erators in generalized orthogonal domain. The new theory is fully tested and verified bythe dynamic analysis l 'modal test and dynamic load identification teSt of a simulation speci-men- It is shown that the method has some advantages, such as the simple dynamic cali-bration test, the high identification accuracy, especially for the transient load with shortsampling. These are very useful in engineering applications.展开更多
Nowadays,the advancement of nonintrusive load monitoring(NILM)has been hastened by the ever-increasing requirements for the reasonable use of electricity by users and demand side management.Although existing researche...Nowadays,the advancement of nonintrusive load monitoring(NILM)has been hastened by the ever-increasing requirements for the reasonable use of electricity by users and demand side management.Although existing researches have tried their best to extract a wide variety of load features based on transient or steady state of electrical appliances,it is still very difficult for their algorithm to model the load decomposition problem of different electrical appliance types in a targeted manner to jointly mine their proposed features.This paper presents a very effective event-driven NILM solution,which aims to separately model different appliance types to mine the unique characteristics of appliances from multi-dimensional features,so that all electrical appliances can achieve the best classification performance.First,we convert the multi-classification problem into a serial multiple binary classification problem through a pre-sort model to simplify the original problem.Then,ConTrastive Loss K-Nearest Neighbour(CTLKNN)model with trainable weights is proposed to targeted mine appliance load characteristics.The simulation results show the effectiveness and stability of the proposed algorithm.Compared with existing algorithms,the proposed algorithm has improved the identification performance of all electrical appliance types.展开更多
A new inductive motors load equivalence algorithm based on coherence is proposed in this paper. In order to partite motors load rapidly and accurately, fuzzy c-means clustering along with particle swarm optimization (...A new inductive motors load equivalence algorithm based on coherence is proposed in this paper. In order to partite motors load rapidly and accurately, fuzzy c-means clustering along with particle swarm optimization (PSO-FCM) algorithm is proposed to identify coherent motors base on its physical essence of fuzziness. The merits of PSO algorithm are independent to initial value and convergent to optimum value rapidly, and the validity function is constructed to assess clustering validity. The test on IEEE 39-Bus System is presented to evaluate the effectiveness of the new algorithm, the membership matrix definite not only coherence group of motors but also correlation value of coherence between motors. The algorithm can be used to partite motor load based on coherency in dynamic equivalence with power system operating on different modes.展开更多
This paper investigates the possibility of utilizing response from natural ice loading for modal parameter identification of real offshore platforms.The test platform is the JZ20-2 MUQ jacket platform located in the L...This paper investigates the possibility of utilizing response from natural ice loading for modal parameter identification of real offshore platforms.The test platform is the JZ20-2 MUQ jacket platform located in the Liaodong Bay,China.A field experiment is carried out in winter season,as the platform is excited by floating ices.The feasibility is demonstrated by the acceleration response of two different segments.By the SSI-data method,the modal frequencies and damping ratios of four structural modes can be successfully identified from both segments.The estimated information from both segments is almost identical,which demonstrates that the modal identification is trustworthy.Furthermore,by taking the Jacket platform as a benchmark,the numerical performance of five popular time-domain EMA methods is systematically compared from different viewpoints.The comparisons are categorized as:(1)stochastic methods versus deterministic methods;(2)high-order methods versus low-order methods;(3)data-driven versus covariance-driven stochastic subspace identification methods.展开更多
Based on risk theory, considering the probability of an accident and the severity of the sequence, combining N-1 and N-2 security check, this paper puts forward a new risk index, which uses the amount of optimal load ...Based on risk theory, considering the probability of an accident and the severity of the sequence, combining N-1 and N-2 security check, this paper puts forward a new risk index, which uses the amount of optimal load shedding as the severity of an accident consequence to identify the critical lines in power system. Taking IEEE24-RTS as an example, the simulation results verify the correctness and effectiveness of the proposed index.展开更多
To locate and quantify local damage in a simply supported bridge, in this study, we derived a rotational-angle influence line equation of a simply supported beam model with local damage. Using the diagram multiplicati...To locate and quantify local damage in a simply supported bridge, in this study, we derived a rotational-angle influence line equation of a simply supported beam model with local damage. Using the diagram multiplication method, we introduce an analytical formula for a novel damage-identification indicator, namely the diff erence of rotational-angle influence linescurvature(DRAIL-C). If the initial stiff ness of the simply supported beam is known, the analytical formula can be effectively used to determine the extent of damage under certain circumstances. We determined the effectiveness and anti-noise performance of this new damage-identification method using numerical examples of a simply supported beam, a simply supported hollow-slab bridge, and a simply supported truss bridge. The results show that the DRAIL-C is directly proportional to the moving concentrated load and inversely proportional to the distance between the bridge support and the concentrated load and the distance between the damaged truss girder and the angle measuring points. The DRAIL-C indicator is more sensitive to the damage in a steel-truss-bridge bottom chord than it is to the other elements.展开更多
Heating,ventilation,and air conditioning(HVAC)systems constitute a significant portion of the office building load and are important flexibility resources.However,the HVAC loads are often inaccessible to the utility o...Heating,ventilation,and air conditioning(HVAC)systems constitute a significant portion of the office building load and are important flexibility resources.However,the HVAC loads are often inaccessible to the utility or load aggregators who only have total load data.Most existing studies require subloads for supervised disaggregation or prior knowledge for unsupervised disaggregation,but such information is hard to obtain.It is necessary to develop an effective,completely unsupervised non-intrusive monitoring method to obtain the HVAC load data.In this study,a multiple seasonal-trend decomposition using the LOESS(MSTL)method is proposed to disaggregate the HVAC load from the total metered electricity data of office buildings.The effects of periodic types(daily,weekly,monthly,etc.),periodic sequences,and parallel/serial structures are analyzed.The proposed method is verified based on the historical electricity data of ten buildings.The results show that the proposed MSTL can accurately disaggregate the HVAC load with a coefficient of variation of the root mean square error(CVRMSE)of 10.94%,a normalized root mean squared error(NRMSE)of 2.1%,and a weighted absolute percentage error(WAPE)of 8.52%.Compared to single-cycle STL,the proposed method can significantly improve load disaggregation performance,with a maximum reduction of 16.36%in CVRMSE,5.3%in NRMSE,and 12.91%in WAPE.Backward-chain-based MSTL is recommended with higher accuracy and robustness.The proposed method provides an effective solution for utilities or load aggregators to improve demand response management and grid stability.展开更多
Identification of impact loads plays important role in marine structures health monitoring but is diffi-cult to be measured directly most time.This study investigates a two-stage framework for impact load localization...Identification of impact loads plays important role in marine structures health monitoring but is diffi-cult to be measured directly most time.This study investigates a two-stage framework for impact load localization and reconstruction,consisting of load region identification and local refined nodal search.For the region identification,a novel frequency response feature preprocessing method based on FFT is proposed and incorporated into a multi-layer perceptron(MLP)neural network as the embedding func-tion of the Matching Network(MN),the core model adopted for pattern recognition.Based on the region probabilities predicted by MN,a local refined nodal search strategy is provided,which is initialized by a region correction method for amending the possible region misclassification and further guided by error metrics with iteration search strategy.Moreover,the inverse problem in this study is formulated in the discretized state space expression with the reduced modal coordinates.For improving the load inverse accuracy affected by Zero Order Hold(ZOH)simplification in this formulation,a dynamic sensor filter strategy is provided.Eventually,a numerical experiment of impact load identification on a steel plate is performed and discussed,whose results indicate the validity and robustness of the proposed method.展开更多
文摘<div style="text-align:justify;"> Load identification method is one of the major technical difficulties of non-intrusive composite monitoring. Binary V-I trajectory image can reflect the original V-I trajectory characteristics to a large extent, so it is widely used in load identification. However, using single binary V-I trajectory feature for load identification has certain limitations. In order to improve the accuracy of load identification, the power feature is added on the basis of the binary V-I trajectory feature in this paper. We change the initial binary V-I trajectory into a new 3D feature by mapping the power feature to the third dimension. In order to reduce the impact of imbalance samples on load identification, the SVM SMOTE algorithm is used to balance the samples. Based on the deep learning method, the convolutional neural network model is used to extract the newly produced 3D feature to achieve load identification in this paper. The results indicate the new 3D feature has better observability and the proposed model has higher identification performance compared with other classification models on the public data set PLAID. </div>
基金funded by Vice Chancellor of Research at Shiraz University(grant 3GFU2M1820).
文摘The identification of the traction acting on a portion of the surface of an anisotropic solid is very important in structural health monitoring and optimal design of structures. The traction can be determined using inverse methods in which displacement or strain measurements are taken at several points on the body. This paper presents an inverse method based on the method of fundamental solutions for the traction identification problem in two-dimensional anisotropic elasticity. The method of fundamental solutions is an efficient boundary-type meshless method widely used for analyzing various problems. Since the problem is linear, the sensitivity analysis is simply performed by solving the corresponding direct problem several times with different loads. The effects of important parameters such as the number of measurement data, the position of the measurement points, the amount of measurement error, and the type of measurement, i.e., displacement or strain, on the results are also investigated. The results obtained show that the presented inverse method is suitable for the problem of traction identification. It can be concluded from the results that the use of strain measurements in the inverse analysis leads to more accurate results than the use of displacement measurements. It is also found that measurement points closer to the boundary with unknown traction provide more reliable solutions. Additionally, it is found that increasing the number of measurement points increases the accuracy of the inverse solution. However, in cases with a large number of measurement points, further increasing the number of measurement data has little effect on the results.
基金supported by the National Natural Science Foundation of China(Grant Nos.52475166,52175148)the Regional Collaboration Project of Shanxi Province(Grant No.202204041101044).
文摘Modern warfare demands weapons capable of penetrating substantial structures,which presents sig-nificant challenges to the reliability of the electronic devices that are crucial to the weapon's perfor-mance.Due to miniaturization of electronic components,it is challenging to directly measure or numerically predict the mechanical response of small-sized critical interconnections in board-level packaging structures to ensure the mechanical reliability of electronic devices in projectiles under harsh working conditions.To address this issue,an indirect measurement method using the Bayesian regularization-based load identification was proposed in this study based on finite element(FE)pre-dictions to estimate the load applied on critical interconnections of board-level packaging structures during the process of projectile penetration.For predicting the high-strain-rate penetration process,an FE model was established with elasto-plastic constitutive models of the representative packaging ma-terials(that is,solder material and epoxy molding compound)in which material constitutive parameters were calibrated against the experimental results by using the split-Hopkinson pressure bar.As the impact-induced dynamic bending of the printed circuit board resulted in an alternating tensile-compressive loading on the solder joints during penetration,the corner solder joints in the edge re-gions experience the highest S11 and strain,making them more prone to failure.Based on FE predictions at different structural scales,an improved Bayesian method based on augmented Tikhonov regulariza-tion was theoretically proposed to address the issues of ill-posed matrix inversion and noise sensitivity in the load identification at the critical solder joints.By incorporating a wavelet thresholding technique,the method resolves the problem of poor load identification accuracy at high noise levels.The proposed method achieves satisfactorily small relative errors and high correlation coefficients in identifying the mechanical response of local interconnections in board-level packaging structures,while significantly balancing the smoothness of response curves with the accuracy of peak identification.At medium and low noise levels,the relative error is less than 6%,while it is less than 10%at high noise levels.The proposed method provides an effective indirect approach for the boundary conditions of localized solder joints during the projectile penetration process,and its philosophy can be readily extended to other scenarios of multiscale analysis for highly nonlinear materials and structures under extreme loading conditions.
基金supported by the National Nature Science Foundation of China(No.12072007)the Ningbo Nature Science Foundation(No.202003N4018)+1 种基金the Aeronautical Science Foundation of China (No. 20182951014)the Defense Industrial Technology Development Program(No.JCKY2019209C004)
文摘The determination of the dynamic load is one of the indispensable technologies for structure design and health monitoring for aerospace vehicles.However,it is a significant challenge to measure the external excitation directly.By contrast,the technique of dynamic load identification based on the dynamic model and the response information is a feasible access to obtain the dynamic load indirectly.Furthermore,there are multi-source uncertainties which cannot be neglected for complex systems in the load identification process,especially for aerospace vehicles.In this paper,recent developments in the dynamic load identification field for aerospace vehicles considering multi-source uncertainties are reviewed,including the deterministic dynamic load identification and uncertain dynamic load identification.The inversion methods with different principles of concentrated and distributed loads,and the quantification and propagation analysis for multi-source uncertainties are discussed.Eventually,several possibilities remaining to be explored are illustrated in brief.
基金Project 59775004 supported by National Natural Science Foundation of China
文摘Based on the platform of Matlab and the theory of digital signal processing, we propose a method in the cepstrum domain for dynamic load spectra identification of machinery. We demonstrate that the dynamic load spectra can be identified from the response signal of the system, based on cepstra. An ARMA model is built based on the harmonic retrieval by high-order spectra. The coefficients of a Green function are determined and the window width can be estimated. Finally the effectiveness of the method is validated by simulation results.
基金supported in part by the National Natural Science Foundation of China(No.51775270)the Project of Qatar National Research Fund(No.NPRP11S-1220-170112)
文摘We introduce the extended Kalman filter(EKF)method combined with the least square estimation to identify the unknown load acting on the time-varying structure and realize the tracking of the structural parameters of the time-varying system.Firstly,we propose the dynamic load identification method when the unknown parameters are stiffness coefficients.Then,a five-degree-of-freedom slowly-varying-stiffness structure is introduced to verify the effectiveness and the accuracy of the EKF method.The results show that the EKF method can accurately identify unknown loads and structural parameters simultaneously even considering noises in the input data.
基金funding for this study from National Key R&D Program of China(2018YFA0702800)National Natural Science Foundation of China(12072056)+1 种基金the Fundamental Research Funds for the Central Universities(DUT19LK49)Nantong Science and Technology Plan Project(No.MS22019016).
文摘For practical engineering structures,it is usually difficult to measure external load distribution in a direct manner,which makes inverse load identification important.Specifically,load identification is a typical inverse problem,for which the models(e.g.,response matrix)are often ill-posed,resulting in degraded accuracy and impaired noise immunity of load identification.This study aims at identifying external loads in a stiffened plate structure,through comparing the effectiveness of different methods for parameter selection in regulation problems,including the Generalized Cross Validation(GCV)method,the Ordinary Cross Validation method and the truncated singular value decomposition method.With demonstrated high accuracy,the GCV method is used to identify concentrated loads in three different directions(e.g.,vertical,lateral and longitudinal)exerted on a stiffened plate.The results show that the GCV method is able to effectively identify multi-source static loads,with relative errors less than 5%.Moreover,under the situation of swept frequency excitation,when the excitation frequency is near the natural frequency of the structure,the GCV method can achieve much higher accuracy compared with direct inversion.At other excitation frequencies,the average recognition error of the GCV method load identification less than 10%.
文摘A dynamic load identification model of structural system based on the gener-alized orthogonal polynomial theory is provided, and the least Square discrete algorithm foridentifying the dynamic load is supplied. The main key is that the convolution relationsbetween the input and output of the system in time domain are transformed into linear oP-erators in generalized orthogonal domain. The new theory is fully tested and verified bythe dynamic analysis l 'modal test and dynamic load identification teSt of a simulation speci-men- It is shown that the method has some advantages, such as the simple dynamic cali-bration test, the high identification accuracy, especially for the transient load with shortsampling. These are very useful in engineering applications.
基金supported by National Natural Science Foundation of China(No.61531007).
文摘Nowadays,the advancement of nonintrusive load monitoring(NILM)has been hastened by the ever-increasing requirements for the reasonable use of electricity by users and demand side management.Although existing researches have tried their best to extract a wide variety of load features based on transient or steady state of electrical appliances,it is still very difficult for their algorithm to model the load decomposition problem of different electrical appliance types in a targeted manner to jointly mine their proposed features.This paper presents a very effective event-driven NILM solution,which aims to separately model different appliance types to mine the unique characteristics of appliances from multi-dimensional features,so that all electrical appliances can achieve the best classification performance.First,we convert the multi-classification problem into a serial multiple binary classification problem through a pre-sort model to simplify the original problem.Then,ConTrastive Loss K-Nearest Neighbour(CTLKNN)model with trainable weights is proposed to targeted mine appliance load characteristics.The simulation results show the effectiveness and stability of the proposed algorithm.Compared with existing algorithms,the proposed algorithm has improved the identification performance of all electrical appliance types.
文摘A new inductive motors load equivalence algorithm based on coherence is proposed in this paper. In order to partite motors load rapidly and accurately, fuzzy c-means clustering along with particle swarm optimization (PSO-FCM) algorithm is proposed to identify coherent motors base on its physical essence of fuzziness. The merits of PSO algorithm are independent to initial value and convergent to optimum value rapidly, and the validity function is constructed to assess clustering validity. The test on IEEE 39-Bus System is presented to evaluate the effectiveness of the new algorithm, the membership matrix definite not only coherence group of motors but also correlation value of coherence between motors. The algorithm can be used to partite motor load based on coherency in dynamic equivalence with power system operating on different modes.
基金financially supported by the National Science Fund for Distinguished Young Scholars(Grant No.51625902)the Major Scientific and Technological Innovation Project of Shandong Province(Grant No.2019JZZY010820)+2 种基金the National Key Research and Development Program of China(Grant No.2019YFC0312404)the National Natural Science Foundation of China(Grant No.51879249)the Taishan Scholars Program of Shandong Province(Grant No.TS201511016)。
文摘This paper investigates the possibility of utilizing response from natural ice loading for modal parameter identification of real offshore platforms.The test platform is the JZ20-2 MUQ jacket platform located in the Liaodong Bay,China.A field experiment is carried out in winter season,as the platform is excited by floating ices.The feasibility is demonstrated by the acceleration response of two different segments.By the SSI-data method,the modal frequencies and damping ratios of four structural modes can be successfully identified from both segments.The estimated information from both segments is almost identical,which demonstrates that the modal identification is trustworthy.Furthermore,by taking the Jacket platform as a benchmark,the numerical performance of five popular time-domain EMA methods is systematically compared from different viewpoints.The comparisons are categorized as:(1)stochastic methods versus deterministic methods;(2)high-order methods versus low-order methods;(3)data-driven versus covariance-driven stochastic subspace identification methods.
基金Technology Major Project of China Southern Power Grid Co.,Ltd.(GZ2014-2-0049).
文摘Based on risk theory, considering the probability of an accident and the severity of the sequence, combining N-1 and N-2 security check, this paper puts forward a new risk index, which uses the amount of optimal load shedding as the severity of an accident consequence to identify the critical lines in power system. Taking IEEE24-RTS as an example, the simulation results verify the correctness and effectiveness of the proposed index.
基金supported by the National Natural Science Foundation of China(Nos.51608245 and 51568041)Natural Science Foundation of Gansu Province(Nos.148RJZA026 and 2014GS02269)
文摘To locate and quantify local damage in a simply supported bridge, in this study, we derived a rotational-angle influence line equation of a simply supported beam model with local damage. Using the diagram multiplication method, we introduce an analytical formula for a novel damage-identification indicator, namely the diff erence of rotational-angle influence linescurvature(DRAIL-C). If the initial stiff ness of the simply supported beam is known, the analytical formula can be effectively used to determine the extent of damage under certain circumstances. We determined the effectiveness and anti-noise performance of this new damage-identification method using numerical examples of a simply supported beam, a simply supported hollow-slab bridge, and a simply supported truss bridge. The results show that the DRAIL-C is directly proportional to the moving concentrated load and inversely proportional to the distance between the bridge support and the concentrated load and the distance between the damaged truss girder and the angle measuring points. The DRAIL-C indicator is more sensitive to the damage in a steel-truss-bridge bottom chord than it is to the other elements.
文摘Heating,ventilation,and air conditioning(HVAC)systems constitute a significant portion of the office building load and are important flexibility resources.However,the HVAC loads are often inaccessible to the utility or load aggregators who only have total load data.Most existing studies require subloads for supervised disaggregation or prior knowledge for unsupervised disaggregation,but such information is hard to obtain.It is necessary to develop an effective,completely unsupervised non-intrusive monitoring method to obtain the HVAC load data.In this study,a multiple seasonal-trend decomposition using the LOESS(MSTL)method is proposed to disaggregate the HVAC load from the total metered electricity data of office buildings.The effects of periodic types(daily,weekly,monthly,etc.),periodic sequences,and parallel/serial structures are analyzed.The proposed method is verified based on the historical electricity data of ten buildings.The results show that the proposed MSTL can accurately disaggregate the HVAC load with a coefficient of variation of the root mean square error(CVRMSE)of 10.94%,a normalized root mean squared error(NRMSE)of 2.1%,and a weighted absolute percentage error(WAPE)of 8.52%.Compared to single-cycle STL,the proposed method can significantly improve load disaggregation performance,with a maximum reduction of 16.36%in CVRMSE,5.3%in NRMSE,and 12.91%in WAPE.Backward-chain-based MSTL is recommended with higher accuracy and robustness.The proposed method provides an effective solution for utilities or load aggregators to improve demand response management and grid stability.
基金supported by National Natural Science Founda-tion of China(Grant No.U2241266,Grant No.51979163 and Grant No.51809168)Marine Equipment Foresight Innovation Union Project(ZCJDQZ202304B02)the Fundamental Research Funds for the Central Universities.
文摘Identification of impact loads plays important role in marine structures health monitoring but is diffi-cult to be measured directly most time.This study investigates a two-stage framework for impact load localization and reconstruction,consisting of load region identification and local refined nodal search.For the region identification,a novel frequency response feature preprocessing method based on FFT is proposed and incorporated into a multi-layer perceptron(MLP)neural network as the embedding func-tion of the Matching Network(MN),the core model adopted for pattern recognition.Based on the region probabilities predicted by MN,a local refined nodal search strategy is provided,which is initialized by a region correction method for amending the possible region misclassification and further guided by error metrics with iteration search strategy.Moreover,the inverse problem in this study is formulated in the discretized state space expression with the reduced modal coordinates.For improving the load inverse accuracy affected by Zero Order Hold(ZOH)simplification in this formulation,a dynamic sensor filter strategy is provided.Eventually,a numerical experiment of impact load identification on a steel plate is performed and discussed,whose results indicate the validity and robustness of the proposed method.
