Estimation of stationary structural system parameters from non-stationary random vibration data: A locally stationary model method

1982 ◽  
Vol 81 (2) ◽  
pp. 215-227 ◽  
Author(s):  
W. Gersch ◽  
T. Brotherton
2011 ◽  
Vol 219-220 ◽  
pp. 1113-1116
Author(s):  
Yong Jie Zhang ◽  
Xiao Hu Zhang ◽  
Bin Tuan Wang

The vibration data processing of environmental spectrum is an important part of environmental reliability test for the aircraft. Based on the inductive methods for environmental measured data of vibration, an inductive arithmetic is employed to estimate the random vibration data of aircraft surface in Matlab system. Numerical experiments show that the inductive estimation arithmetic is effective, reliable and applicable for the environmental measured data of random vibration.


2014 ◽  
Vol 936 ◽  
pp. 2098-2102
Author(s):  
Hong Yan Wang ◽  
Qin Long Wang ◽  
Qiang Rui ◽  
Wei Biao Ma

In this article a surrogate model between suspension system parameters of tracked vehicle and comfort evaluation indices has been built up by using radial basis function method and the dynamic simulation of tracked vehicle. Then the optimization of suspension system parameters has been carried out by using surrogate model and adaptive simulated annealing optimization arithmetic. The results show that the research method is feasible.


2003 ◽  
Vol 47 (04) ◽  
pp. 299-312
Author(s):  
Akhilesh Jha ◽  
Efstratios Nikolaidis ◽  
Sathya Gangadhararn

A special class of nonstationary processes with periodically varying statistics, called cyclostationary (CS), is investigated. This process is encountered in many engineering problems involving rotating machinery, such as turbines, propellers, helicopter rotors, and diesel engines. The objective of this paper is to show that a CS model of the flow-induced excitation on a propeller can describe the physics of the problem more accurately than a traditional stationary model. The mean values of the hydrodynamic forces are calculated using the vortex panel method and the vortex theory of propellers. Considering the randomness in the axial and the tangential components of the wake velocity, we calculate the covariance matrix of the forces. This analysis shows that the hydrodynamic forces acting on the propeller are CS processes. Then we calculate the standard deviation (root mean square [RMS]) of the blade response. We show that the CS model predicts the timewise variation of the statistics of the excitation and the response (e.g., the RMS), including their peaks. A traditional stationary model cannot provide this information because it assumes constant statistics. Finally, a parametric analysis is performed to demonstrate the effects of the correlation structure of the velocity field behind a ship hull on the RMS of the blade deflection.


2015 ◽  
Vol 37 (4) ◽  
pp. 4313-1-4313-6
Author(s):  
Everaldo de Barros ◽  
Carlos d'Andrade Souto ◽  
Leandro Ribeiro de Camargo ◽  
Mauro Hugo Mathias

This paper presents a data acquisition and analysis system based on a computer sound card for measuring and processing random vibration signals. This system turns the computer into a two-channel measurement instrument which provides sample rate, simultaneous sampling, frequency range, filters and others essential capabilities required to perform random vibrations measurements. An easy-to-use software was developed for vibration monitoring and analysis, including facilities for data recording, digital signal processing and real time spectrum analyzer. Since the tasks of vibration data acquisition frequently require expensive hardware and software, this versatile system provides students a very accurate and inexpensive solution for experimental studying mechanical vibrations.


1998 ◽  
Vol 120 (3) ◽  
pp. 806-813 ◽  
Author(s):  
P. D. Spanos ◽  
B. A. Zeldin

The Random Decrement method used in system identification for analysis of random vibration data is considered from a rigorous mathematical perspective. It is shown that the Random Decrement signature deviates from the system free vibration curve of an associated linear system, unless the corresponding input excitation is white. The error induced by approximating the system excitation by a white noise process is examined. Further, a generalized Random Decrement signature is introduced; it is used to estimate efficiently the auto-correlation function of an ergodic Gaussian random process. Several examples are discussed to elucidate the theoretical developments.


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