Modal parameter identification is a mature technology.However,there are some challenges in its practical applications such as the identification of vibration systems involving closely spaced modes and intensive noise ...Modal parameter identification is a mature technology.However,there are some challenges in its practical applications such as the identification of vibration systems involving closely spaced modes and intensive noise contamination.This paper proposes a new time-frequency method based on intrinsic chirp component decomposition(ICCD)to address these issues.In this method,a redundant Fourier model is used to ameliorate border distortions and improve the accuracy of signal reconstruction.The effectiveness and accuracy of the proposed method are illustrated using three examples:a cantilever beam structure with intensive noise contamination or environmental interference,a four-degree-of-freedom structure with two closely spaced modes,and an impact test on a cantilever rectangular plate.By comparison with the identification method based on the empirical wavelet transform(EWT),it is shown that the presented method is effective,even in a high-noise environment,and the dynamic characteristics of closely spaced modes are accurately determined.展开更多
Time-varying systems are applied extensively in practical applications,and their related parameter identification techniques are of great significance for structural health monitoring of time-varying systems.To improv...Time-varying systems are applied extensively in practical applications,and their related parameter identification techniques are of great significance for structural health monitoring of time-varying systems.To improve the identification accuracy for time-varying systems,this study puts forward a novel parameter identification approach in the time-frequency domain using intrinsic chirp component decomposition(ICCD).ICCD is a powerful tool for signal decomposition and parameter extraction,with good signal reconstruction capability in a high-noise environment.To maintain good identification effects for the time-varying system in a noisy environment,the proposed method introduces a redundant Fourier model for the non-stationary signal,including instantaneous frequency(IF)and instantaneous amplitude(IA).The accuracy and effectiveness of the proposed approach are demonstrated by a single-degree-of-freedom system with three types of time-varying parameters,as well as an example of a multi-degree-of-freedom system.The effects of different levels of measured noise on the identified results are also discussed in detail.Numerical results show that the proposed method is very effective in tracking the smooth,periodical,and non-smooth variations of time-varying systems over the entire identification time period even when the response signal is contaminated by intense noise.展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.11702170,11320011,and 11802279)the China Postdoctoral Science Foundation(No.2016M601585)
文摘Modal parameter identification is a mature technology.However,there are some challenges in its practical applications such as the identification of vibration systems involving closely spaced modes and intensive noise contamination.This paper proposes a new time-frequency method based on intrinsic chirp component decomposition(ICCD)to address these issues.In this method,a redundant Fourier model is used to ameliorate border distortions and improve the accuracy of signal reconstruction.The effectiveness and accuracy of the proposed method are illustrated using three examples:a cantilever beam structure with intensive noise contamination or environmental interference,a four-degree-of-freedom structure with two closely spaced modes,and an impact test on a cantilever rectangular plate.By comparison with the identification method based on the empirical wavelet transform(EWT),it is shown that the presented method is effective,even in a high-noise environment,and the dynamic characteristics of closely spaced modes are accurately determined.
文摘Time-varying systems are applied extensively in practical applications,and their related parameter identification techniques are of great significance for structural health monitoring of time-varying systems.To improve the identification accuracy for time-varying systems,this study puts forward a novel parameter identification approach in the time-frequency domain using intrinsic chirp component decomposition(ICCD).ICCD is a powerful tool for signal decomposition and parameter extraction,with good signal reconstruction capability in a high-noise environment.To maintain good identification effects for the time-varying system in a noisy environment,the proposed method introduces a redundant Fourier model for the non-stationary signal,including instantaneous frequency(IF)and instantaneous amplitude(IA).The accuracy and effectiveness of the proposed approach are demonstrated by a single-degree-of-freedom system with three types of time-varying parameters,as well as an example of a multi-degree-of-freedom system.The effects of different levels of measured noise on the identified results are also discussed in detail.Numerical results show that the proposed method is very effective in tracking the smooth,periodical,and non-smooth variations of time-varying systems over the entire identification time period even when the response signal is contaminated by intense noise.
