Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from b...Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from both academia and industry.However,the extensive literature that exists on this topic does not address identifying the severity of actuator faults and focuses mainly on actuator fault detection and isolation.In addition,previous studies of actuator fault identification have not dealt with multiple concurrent faults in real time,especially when these are accompanied by sudden failures under dynamic conditions.This study develops component-level models for fault identification in four typical actuators used in high-bypass ratio turbofan engines under both dynamic and steady-state conditions and these are then integrated with the engine performance model developed by the authors.The research results reported here present a novel method of quantifying actuator faults using dynamic effect compensation.The maximum error for each actuator is less than0.06%and 0.07%,with average computational time of less than 0.0058 s and 0.0086 s for steady-state and transient cases,respectively.These results confirm that the proposed method can accurately and efficiently identify concurrent actuator fault for an engine operating under either transient or steady-state conditions,even in the case of a sudden malfunction.The research results emonstrate the potential benefit to emergency response capabilities by introducing this method of monitoring the health of aero engines.展开更多
A new method is proposed to assess the condition of structures under unknown support excitation by simultaneously detecting local damage and identifying the support excitation from several structural dynamic responses...A new method is proposed to assess the condition of structures under unknown support excitation by simultaneously detecting local damage and identifying the support excitation from several structural dynamic responses. The support excitation acting on a structure is modeled by orthogonal polynomial approximations, and the sensitivities of structural dynamic response with respect to its physical parameters and orthogonal coefficients are derived. The identification equation is based on Taylor's first order approximation, and is solved with the damped least-squares method in an iterative procedure. A fifteen-story shear building model and a five-story three-dimensional steel frame structure are studied to validate the proposed method. Numerical simulations with noisy measured accelerations show that the proposed method can accurately detect local damage and identify unknown support excitation from only several responses of the structure. This method provides a new approach for detecting structural damage and updating models with unknown input and incomplete measured output information.展开更多
Complete vehicle trajectory data is essential for urban traffic flow modeling studies.This study proposes a framework for filling vehicle trajectories in spatial and time for automatic vehicle identification(AVl)data....Complete vehicle trajectory data is essential for urban traffic flow modeling studies.This study proposes a framework for filling vehicle trajectories in spatial and time for automatic vehicle identification(AVl)data.Based on the particle filter,the dynamic correction factor is innovatively used to improve algorithm accuracy.After four resamplings,such as traffic situation index and traffic event factor,spatial trajectory filling is completed.The Copula function fills the time trajectory by analyzing the correlation between upstream and downstream paths.Finally,the experiment was conducted in Xiaoshan District,Hangzhou,China.The results show that for spatial trajectory filling,the average accuracy exceeds 97%with 75%camera coverage.In time trajectory filling,the time trajectory filling error is reduced by 35%compared to the Hellinga algorithm.展开更多
随着常规高压直流输电系统(line commutated converter based high voltage direct current,LCC-HVDC)受端的电力电子设备渗透率增加,并网点电气信号呈时变特征,导致系统宽频谐振风险日益突出。针对LCC-HVDC受端网侧灰箱化背景下传统阻...随着常规高压直流输电系统(line commutated converter based high voltage direct current,LCC-HVDC)受端的电力电子设备渗透率增加,并网点电气信号呈时变特征,导致系统宽频谐振风险日益突出。针对LCC-HVDC受端网侧灰箱化背景下传统阻抗辨识方法工况适应性差、量测频带窄的问题,提出一种受端电网多工况宽频阻抗非侵入动态辨识方法。首先,通过建立LCC-HVDC受端等效宽频阻抗模型,基于并网点端口信号,利用同步挤压小波变换提取宽频动态特征。然后,采用改进卡尔曼滤波算法实现特征信息的快速融合与阻抗时频域估计,从而无需外源扰动即可动态追踪电网宽频阻抗。最后,对LCC-HVDC系统的宽频阻抗及谐振风险进行辨识,并利用广义奈奎斯特曲线与时域仿真方法,验证阻抗特征辨识与宽频谐振风险评估的有效性。展开更多
基金support by the National Natural Science Foundation of China(Grant No.52402520)。
摘要Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from both academia and industry.However,the extensive literature that exists on this topic does not address identifying the severity of actuator faults and focuses mainly on actuator fault detection and isolation.In addition,previous studies of actuator fault identification have not dealt with multiple concurrent faults in real time,especially when these are accompanied by sudden failures under dynamic conditions.This study develops component-level models for fault identification in four typical actuators used in high-bypass ratio turbofan engines under both dynamic and steady-state conditions and these are then integrated with the engine performance model developed by the authors.The research results reported here present a novel method of quantifying actuator faults using dynamic effect compensation.The maximum error for each actuator is less than0.06%and 0.07%,with average computational time of less than 0.0058 s and 0.0086 s for steady-state and transient cases,respectively.These results confirm that the proposed method can accurately and efficiently identify concurrent actuator fault for an engine operating under either transient or steady-state conditions,even in the case of a sudden malfunction.The research results emonstrate the potential benefit to emergency response capabilities by introducing this method of monitoring the health of aero engines.
基金National Natural Science Foundation of China Under Grant No.50579008Joint Research Fund for Overseas Chinese, Hong Kong and Macao Young Scholars Under Grant No.50429802+1 种基金Program for New Century Excellent Talents in University by State Education Commission Under Grant No.NCET-04-0323a research grant from the Hong Kong Polytechnic University
摘要A new method is proposed to assess the condition of structures under unknown support excitation by simultaneously detecting local damage and identifying the support excitation from several structural dynamic responses. The support excitation acting on a structure is modeled by orthogonal polynomial approximations, and the sensitivities of structural dynamic response with respect to its physical parameters and orthogonal coefficients are derived. The identification equation is based on Taylor's first order approximation, and is solved with the damped least-squares method in an iterative procedure. A fifteen-story shear building model and a five-story three-dimensional steel frame structure are studied to validate the proposed method. Numerical simulations with noisy measured accelerations show that the proposed method can accurately detect local damage and identify unknown support excitation from only several responses of the structure. This method provides a new approach for detecting structural damage and updating models with unknown input and incomplete measured output information.
基金supported by National Natural Science Foundation of China Key Project(52131202)National Natural Science Foundation of China General Project(52272314)+1 种基金Humanities and Social Sciences General Project of the Ministry of Education(21YJCZH116)Zhejiang Public Welfare Technology Research Program(LGF22E080007).
摘要Complete vehicle trajectory data is essential for urban traffic flow modeling studies.This study proposes a framework for filling vehicle trajectories in spatial and time for automatic vehicle identification(AVl)data.Based on the particle filter,the dynamic correction factor is innovatively used to improve algorithm accuracy.After four resamplings,such as traffic situation index and traffic event factor,spatial trajectory filling is completed.The Copula function fills the time trajectory by analyzing the correlation between upstream and downstream paths.Finally,the experiment was conducted in Xiaoshan District,Hangzhou,China.The results show that for spatial trajectory filling,the average accuracy exceeds 97%with 75%camera coverage.In time trajectory filling,the time trajectory filling error is reduced by 35%compared to the Hellinga algorithm.
摘要随着常规高压直流输电系统(line commutated converter based high voltage direct current,LCC-HVDC)受端的电力电子设备渗透率增加,并网点电气信号呈时变特征,导致系统宽频谐振风险日益突出。针对LCC-HVDC受端网侧灰箱化背景下传统阻抗辨识方法工况适应性差、量测频带窄的问题,提出一种受端电网多工况宽频阻抗非侵入动态辨识方法。首先,通过建立LCC-HVDC受端等效宽频阻抗模型,基于并网点端口信号,利用同步挤压小波变换提取宽频动态特征。然后,采用改进卡尔曼滤波算法实现特征信息的快速融合与阻抗时频域估计,从而无需外源扰动即可动态追踪电网宽频阻抗。最后,对LCC-HVDC系统的宽频阻抗及谐振风险进行辨识,并利用广义奈奎斯特曲线与时域仿真方法,验证阻抗特征辨识与宽频谐振风险评估的有效性。