scholarly journals Robust Time-Varying Parameter Identification for Composite Load Modeling

2019 ◽  
Vol 10 (1) ◽  
pp. 967-979 ◽  
Author(s):  
Chong Wang ◽  
Zhaoyu Wang ◽  
Jianhui Wang ◽  
Dongbo Zhao
2019 ◽  
Vol 10 (6) ◽  
pp. 6102-6114 ◽  
Author(s):  
Mingjian Cui ◽  
Mahdi Khodayar ◽  
Chen Chen ◽  
Xinan Wang ◽  
Ying Zhang ◽  
...  

2020 ◽  
Vol 20 (07) ◽  
pp. 2050077
Author(s):  
Chao Wang ◽  
Jing Zhang ◽  
Hong Pin Zhu

Time-varying parameter identification is essential for structural health monitoring and performance evaluation. In this paper, a combined method based on the variational mode decomposition and generalized Morse wavelet is proposed to identify the structural time-varying parameters. Based on the sparse property of structural response signals in wavelet domain, a fast iterative shrinkage-thresholding algorithm is adopted to reduce the noise. Then the de-noised signal is decomposed into multi- modes by the variational mode decomposition, and the generalized Morse wavelet is performed to identify the instantaneous frequency. To validate the proposed method, a numerical example including different frequency variations is studied. Experimental validations of a moving vehicle across a bridge and a time-varying cable system considering two patterns of cable tension variations in the laboratory are carried out to investigate the capability of the proposed approach. It is confirmed that the proposed approach can effectively perform the signal decomposition, while identifying the instantaneous frequencies of the time-varying systems accurately.


2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Sudhir Kaul

This paper presents four alternate models of varying complexity to examine mechanical snubbing in elastomeric isolators. Although the modeling, analysis, and experimentation presented is limited to snubbing of elastomeric isolators, the models are generic and can be adapted to other snubbing mechanisms as well, such as friction snubbing. Two of the four models presented in this paper use the Bouc–Wen model in order to capture hysteresis and gradual stiffening behavior, which is generally exhibited by elastomeric snubbing systems. The other two models are relatively simplistic and do not account for a time-varying parameter to model significant hysteresis. However, these two models can still be useful for applications with a small range of excitation frequencies and for applications where the snubbing design needs to incorporate an abrupt transition in stiffness. A parameter identification technique is used to determine the variables associated with each model. The parameter identification technique is based on the use of an optimization algorithm associated with the force–displacement characterization. All four models presented in this paper capture the coupled dynamics of the isolation system and the snubbing system and are, therefore, a significant improvement upon the currently used models. The models presented are expected to facilitate the design and analysis of a passive isolation system in conjunction with the design of the snubbing system and the base frame supporting the snubbing system.


2013 ◽  
Vol 344 ◽  
pp. 205-209
Author(s):  
Jing Bo Gao ◽  
Xiao Dan Wang ◽  
Wei Yao Zhang ◽  
Cong Wang

In this paper, two kinds of time varying parameter identification methods which are called identification method of using broken line to approximate time varying parameter and identification algorithm with auto-regulation forgetting factor are studied. The two methods are used for short-term time-varying system parameter identification in simulations. According to the results of the simulations, the applicable conditions of the two kinds of identification methods are analyzed. The results of the simulations indicate the effectiveness of the two methods. Furthermore whether the two kinds of identification methods are sensitive to noise or not is studied by setting different noise levels in the simulations. Finally, experiments of a variable mass cylindrical shell are adopted to demonstrate the efficiency of the two kinds of time varying parameter identification methods.


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