Multi-radar signal separation is a critical process in modern reconnaissance systems. However, the complicated battlefield is typically confronted with increasing electronic equipment and complex radar waveforms. The ...Multi-radar signal separation is a critical process in modern reconnaissance systems. However, the complicated battlefield is typically confronted with increasing electronic equipment and complex radar waveforms. The intercepted signal is difficult to separate with conventional parameters because of severe overlapping in both time and frequency domains. On the contrary, time-frequency analysis maps the 1D signal into a 2D time-frequency plane, which provides a better insight into the signal than traditional methods. Particularly, the parameterized time-frequency analysis (PTFA) shows great potential in processing such non stationary signals. Five procedures for the PTFA are proposed to separate the overlapped multi-radar signal, including initiation, instantaneous frequency estimation with PTFA, signal demodulation, signal separation with adaptive filter and signal recovery. The proposed method is verified with both simulated and real signals, which shows good performance in the application on multi-radar signal separation.展开更多
In this paper, we propose a cross reference method for nonlinear time series analyzing in semi blind case, that is, the dynamic equations modeling the time series are known but the corresponding parameters are not. ...In this paper, we propose a cross reference method for nonlinear time series analyzing in semi blind case, that is, the dynamic equations modeling the time series are known but the corresponding parameters are not. The tasks of noise reduction and parameter estimation which were fulfilled separately before are combined iteratively. With the positive interaction between the two processing modules, the method is somewhat superior. Some prior work can be viewed as special cases of this general framework. The simulations for noise reduction and parameter estimation of contaminated chaotic time series show improved performance of our method compared with previous work.展开更多
Blade-health monitoring is intensely required for turbomachinery because of the high failure risk of rotating blades.Blade-Tip Timing(BTT)is considered as the most promising technique for operational blade-vibration m...Blade-health monitoring is intensely required for turbomachinery because of the high failure risk of rotating blades.Blade-Tip Timing(BTT)is considered as the most promising technique for operational blade-vibration monitoring,which obtains the parameters that characterize the blade condition from recorded signals.However,its application is hindered by severe undersampling and stringent probe layouts.An inappropriate probe layout can make most of the existing methods invalid or inaccurate.Additionally,a general conflict arises between the allowed and required layouts because of arrangement restrictions.For the sake of economy and safety,parameter identification based on fewer probes has been preferred by users.In this work,a spatial-transformation-based method for parameter identification is proposed based on a single-probe BTT measurement.To present the general Sampling-Aliasing Frequency(SAFE)map definition,the traditional time-frequency analysis methods are extended to a time-sampling frequency.Then,a SAFE map is projected onto a parameter space using spatial transformation to extract the slope and intercept parameters,which can be physically interpreted as an engine order and a natural frequency using coordinate transformation.Finally,the effectiveness and robustness of the proposed method are verified by simulations and experiments under uniformly and nonuniformly variable speed conditions.展开更多
The vibration caused blade High Cycle Fatigue(HCF)is seriously affects the safety operation of turbomachinery especially for aero-engine.Thus,it is crucial important to identify the blade vibration parameters and then...The vibration caused blade High Cycle Fatigue(HCF)is seriously affects the safety operation of turbomachinery especially for aero-engine.Thus,it is crucial important to identify the blade vibration parameters and then evaluate the dynamic stress amplitude.Blade Tip Timing(BTT)method is one of the promising method to solve these problems.While,it need a high resolution Once Per Revolution(OPR)signal which is difficult to get for the aero-engine.Here,a Coupled Vibration Analysis(CVA)method for identifying blade vibration parameters by a none OPR BTT is proposed.The method assumes that every real blade has its own vibration performance at a given speed.Whereby,it can take any blade as the reference blade,and the other blades using the reference blade as the OPR for vibration displacement calculating and further parameter identifying.The proposed method is validated by numerical model.Also,experimental studies are carried out on a straight blade and a twisted three dimensional blade test rig as well as a large industrial axial compressor respectively.The results show that the proposed method can accurately identify the blade synchronous vibration parameters and quantitatively evaluate the mistuning in bladed disks,which lays a foundation for the reliability improvement of aero-engine.展开更多
In most of real operational conditions only response data are measurable while the actual excitations are unknown, so modal parameter must be extracted only from responses. This paper gives a theoretical formulation f...In most of real operational conditions only response data are measurable while the actual excitations are unknown, so modal parameter must be extracted only from responses. This paper gives a theoretical formulation for the cross-correlation functions and cross-power spectra between the outputs under the assumption of white-noise excitation. It widens the field of modal analysis under ambient excitation because many classical methods by impulse response functions or frequency response functions can be used easily for modal analysis under unknown excitation. The Polyreference Complex Exponential method and Eigensystem Realization Algorithm using cross-correlation functions in time domain and Orthogonal Polynomial method using cross-power spectra in frequency domain are applied to a steel frame to extract modal parameters under operational conditions. The modal properties of the steel frame from these three methods are compared with those from frequency response functions analysis. The results show that the modal analysis method using cross-correlation functions or cross-power spectra presented in this paper can extract modal parameters efficiently under unknown excitation.展开更多
This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) ...This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.展开更多
This study examines the feasibility of using a machine learning approach for rapid damage assessment of rein-forced concrete(RC)buildings after the earthquake.Since the real-world damaged datasets are lacking,have lim...This study examines the feasibility of using a machine learning approach for rapid damage assessment of rein-forced concrete(RC)buildings after the earthquake.Since the real-world damaged datasets are lacking,have limited access,or are imbalanced,a simulation dataset is prepared by conducting a nonlinear time history analy-sis.Different machine learning(ML)models are trained considering the structural parameters and ground motion characteristics to predict the RC building damage into five categories:null,slight,moderate,heavy,and collapse.The random forest classifier(RFC)has achieved a higher prediction accuracy on testing and real-world damaged datasets.The structural parameters can be extracted using different means such as Google Earth,Open Street Map,unmanned aerial vehicles,etc.However,recording the ground motion at a closer distance requires the installation of a dense array of sensors which requires a higher cost.For places with no earthquake recording station/device,it is difficult to have ground motion characteristics.For that different ML-based regressor models are developed utilizing past-earthquake information to predict ground motion parameters such as peak ground acceleration and peak ground velocity.The random forest regressor(RFR)achieved better results than other regression models on testing and validation datasets.Furthermore,compared with the results of similar research works,a better result is obtained using RFC and RFR on validation datasets.In the end,these models are uti-lized to predict the damage categories of RC buildings at Saitama University and Okubo Danchi,Saitama,Japan after an earthquake.This damage information is crucial for government agencies or decision-makers to respond systematically in post-disaster situations.展开更多
文摘Multi-radar signal separation is a critical process in modern reconnaissance systems. However, the complicated battlefield is typically confronted with increasing electronic equipment and complex radar waveforms. The intercepted signal is difficult to separate with conventional parameters because of severe overlapping in both time and frequency domains. On the contrary, time-frequency analysis maps the 1D signal into a 2D time-frequency plane, which provides a better insight into the signal than traditional methods. Particularly, the parameterized time-frequency analysis (PTFA) shows great potential in processing such non stationary signals. Five procedures for the PTFA are proposed to separate the overlapped multi-radar signal, including initiation, instantaneous frequency estimation with PTFA, signal demodulation, signal separation with adaptive filter and signal recovery. The proposed method is verified with both simulated and real signals, which shows good performance in the application on multi-radar signal separation.
文摘In this paper, we propose a cross reference method for nonlinear time series analyzing in semi blind case, that is, the dynamic equations modeling the time series are known but the corresponding parameters are not. The tasks of noise reduction and parameter estimation which were fulfilled separately before are combined iteratively. With the positive interaction between the two processing modules, the method is somewhat superior. Some prior work can be viewed as special cases of this general framework. The simulations for noise reduction and parameter estimation of contaminated chaotic time series show improved performance of our method compared with previous work.
基金supported by the National Key Research and Development Program of China(No.2020YFB2010800)the National Natural Science Foundation of China(Nos.51875433 and 92060302)+1 种基金the Natural Science Foundation of Shaanxi Province,China(No.2019KJXX-043,2021JC-04)the Fundamental Research Funds for the Central Universities and the Foundation of Beilin District,China(No.GX2029)。
文摘Blade-health monitoring is intensely required for turbomachinery because of the high failure risk of rotating blades.Blade-Tip Timing(BTT)is considered as the most promising technique for operational blade-vibration monitoring,which obtains the parameters that characterize the blade condition from recorded signals.However,its application is hindered by severe undersampling and stringent probe layouts.An inappropriate probe layout can make most of the existing methods invalid or inaccurate.Additionally,a general conflict arises between the allowed and required layouts because of arrangement restrictions.For the sake of economy and safety,parameter identification based on fewer probes has been preferred by users.In this work,a spatial-transformation-based method for parameter identification is proposed based on a single-probe BTT measurement.To present the general Sampling-Aliasing Frequency(SAFE)map definition,the traditional time-frequency analysis methods are extended to a time-sampling frequency.Then,a SAFE map is projected onto a parameter space using spatial transformation to extract the slope and intercept parameters,which can be physically interpreted as an engine order and a natural frequency using coordinate transformation.Finally,the effectiveness and robustness of the proposed method are verified by simulations and experiments under uniformly and nonuniformly variable speed conditions.
基金supported financially by Natural Science Foundation of China(Nos.51775030,91860126)the Fundamental Research Funds for the Central Universities(No.BHYC1703A)。
文摘The vibration caused blade High Cycle Fatigue(HCF)is seriously affects the safety operation of turbomachinery especially for aero-engine.Thus,it is crucial important to identify the blade vibration parameters and then evaluate the dynamic stress amplitude.Blade Tip Timing(BTT)method is one of the promising method to solve these problems.While,it need a high resolution Once Per Revolution(OPR)signal which is difficult to get for the aero-engine.Here,a Coupled Vibration Analysis(CVA)method for identifying blade vibration parameters by a none OPR BTT is proposed.The method assumes that every real blade has its own vibration performance at a given speed.Whereby,it can take any blade as the reference blade,and the other blades using the reference blade as the OPR for vibration displacement calculating and further parameter identifying.The proposed method is validated by numerical model.Also,experimental studies are carried out on a straight blade and a twisted three dimensional blade test rig as well as a large industrial axial compressor respectively.The results show that the proposed method can accurately identify the blade synchronous vibration parameters and quantitatively evaluate the mistuning in bladed disks,which lays a foundation for the reliability improvement of aero-engine.
基金Item of the 9-th F ive Plan of the Aeronautical Industrial Corporation
文摘In most of real operational conditions only response data are measurable while the actual excitations are unknown, so modal parameter must be extracted only from responses. This paper gives a theoretical formulation for the cross-correlation functions and cross-power spectra between the outputs under the assumption of white-noise excitation. It widens the field of modal analysis under ambient excitation because many classical methods by impulse response functions or frequency response functions can be used easily for modal analysis under unknown excitation. The Polyreference Complex Exponential method and Eigensystem Realization Algorithm using cross-correlation functions in time domain and Orthogonal Polynomial method using cross-power spectra in frequency domain are applied to a steel frame to extract modal parameters under operational conditions. The modal properties of the steel frame from these three methods are compared with those from frequency response functions analysis. The results show that the modal analysis method using cross-correlation functions or cross-power spectra presented in this paper can extract modal parameters efficiently under unknown excitation.
基金Project (Nos. 60174009 and 70071017) supported by the NationalNatural Science Foundation of China
文摘This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.
文摘This study examines the feasibility of using a machine learning approach for rapid damage assessment of rein-forced concrete(RC)buildings after the earthquake.Since the real-world damaged datasets are lacking,have limited access,or are imbalanced,a simulation dataset is prepared by conducting a nonlinear time history analy-sis.Different machine learning(ML)models are trained considering the structural parameters and ground motion characteristics to predict the RC building damage into five categories:null,slight,moderate,heavy,and collapse.The random forest classifier(RFC)has achieved a higher prediction accuracy on testing and real-world damaged datasets.The structural parameters can be extracted using different means such as Google Earth,Open Street Map,unmanned aerial vehicles,etc.However,recording the ground motion at a closer distance requires the installation of a dense array of sensors which requires a higher cost.For places with no earthquake recording station/device,it is difficult to have ground motion characteristics.For that different ML-based regressor models are developed utilizing past-earthquake information to predict ground motion parameters such as peak ground acceleration and peak ground velocity.The random forest regressor(RFR)achieved better results than other regression models on testing and validation datasets.Furthermore,compared with the results of similar research works,a better result is obtained using RFC and RFR on validation datasets.In the end,these models are uti-lized to predict the damage categories of RC buildings at Saitama University and Okubo Danchi,Saitama,Japan after an earthquake.This damage information is crucial for government agencies or decision-makers to respond systematically in post-disaster situations.
