Time-Varying Motion Filtering for Vision-Based Nonstationary Vibration Measurement

2020 ◽  
Vol 69 (6) ◽  
pp. 3907-3916
Author(s):  
Zhen Liu ◽  
Qingbo He ◽  
Shiqian Chen ◽  
Zhike Peng ◽  
Wenming Zhang
Keyword(s):  
Vibration Measurement ◽  
Time Varying ◽  
Nonstationary Vibration

Nonstationary Vibration Signal Analysis Using Wavelet-Based Time–Frequency Filter and Wigner–Ville Distribution

2016 ◽  
Vol 138 (5) ◽  
Author(s):  
Chang Xu ◽  
Cong Wang ◽  
Wei Liu
Keyword(s):  
Method Comparison ◽  
Frequency Resolution ◽  
Time Varying ◽  
Frequency Filter ◽  
Time Frequency ◽  
Nonstationary Vibration ◽  
Comparison Results ◽  
Cross Terms ◽  
Vibration Signal Analysis ◽  
Wheel Vibration

Vibration responses of nonlinear or time-varying dynamical systems are always nonstationary. Time–frequency representation becomes a necessary approach to analysis such signals. In this paper, a nonstationary vibration analysis method based on continuous wavelet transform (CWT) and Wigner–Ville distribution (WVD) is presented. In order to avoid the cross-terms in the original WVD, a time–frequency filter created by wavelet spectrum is employed to filter the time–frequency distribution (TFD). This process eliminates cross-terms and maintains high time–frequency resolution. The improved WVD is applied to both simulated and practical time-varying systems. Bat echolocation signal, train wheel vibration, and bridge vibration under a moving train are used to assess the proposed method. Comparison results show that the improved WVD is free of cross-terms, effective in identifying time-varying frequencies and is more accurate than the wavelet time–frequency spectrum.

scholarly journals Moving Window Differential Evolution Independent Component Analysis-Based Operational Modal Analysis for Slow Linear Time-Varying Structures

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yongshuo Zong ◽  
Jinling Chen ◽  
Siyi Tao ◽  
Cheng Wang ◽  
Jianbing Xiahou
Keyword(s):  
Independent Component Analysis ◽  
Differential Evolution ◽  
Linear Time ◽  
Component Analysis ◽  
Independent Component ◽  
Modal Parameters ◽  
Time Varying ◽  
Moving Window ◽  
Response Measurement ◽  
Nonstationary Vibration

In order to identify time-varying transient modal parameters only from nonstationary vibration response measurement signals for slow linear time-varying (SLTV) structures which are weakly damped, a moving window differential evolution (DE) independent component analysis- (ICA-) based operational modal analysis (OMA) method is proposed in this paper. Firstly, in order to overcome the problems in traditional ICA-based OMA, such as easy to go into local optima and difficult-to-identify high-order modal parameters, we combine DE with ICA and propose a differential evolution independent component analysis- (DEICA-) based OMA method for linear time invariant (LTI) structures. Secondly, we combine the moving widow technique with DEICA and propose a moving window differential evolution independent component analysis- (MWDEICA-) based OMA method for SLTV structures. The MWDEICA-based OMA method has high global searching ability, robustness, and complexity of time and space. The modal identification results in a three-degree-of-freedom structure with slow time-varying mass show that this MWDEICA-based OMA method can identify transient time-varying modal parameters effectively only from nonstationary vibration response measurement signals and has better performances than moving window traditional ICA-based OMA.

Time-varying Macroeconomic Risk of Real Estate Returns

2016 ◽  
Author(s):  
Felix Schindler ◽  
Bertram Steininger ◽  
Tim Kroencke
Keyword(s):  
Real Estate ◽  
Time Varying ◽  
Macroeconomic Risk ◽  
Real Estate Returns
Sign in / Sign up

Export Citation Format

Share Document