Analysis of low damping ratios in multi-exciter stationary non-Gaussian random vibration control

Zheng, Ronghui; Qin, Min; Angeli, Andrea; Vandepitte, Dirk

doi:10.1177/1077546319898561

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…Aerospace products during the service may experience structural integrity damage, performance degradation, fatigue, or failure due to the suffered vibration environments (Aykan and Celik, 2009; Zheng et al, 2019a). In aerospace engineering, multi-shaker vibration tests are usually selected to provide or replicate the multi-dimensional dynamic environments especially for large products such as rockets and missiles (Roberts and Ewins, 2018; Zheng et al, 2020). It is becoming more popular in dynamic test communities in light of its unique advantages and some multi-shaker vibration test guidelines have been evolved (IEST-RP-DTE022, 2014; MIL-STD-810H, 2019).…”

Section: Introductionmentioning

confidence: 99%

Multi-shaker shock response spectra replication control

Zheng

Yan

Wei

et al. 2022

Journal of Vibration and Control

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Shock vibration environments frequently occur and threaten the reliability of products in aerospace engineering. This work deals with the shock response spectra replication control method for multi-shaker vibration test systems. Two synthetic methods for reconstitution of shock response signals with specified shock response spectra are presented. One is the random delay superposition method and the other is the filtering superposition method. The former utilizes a random time delay vector satisfying a normal distribution to simulate a shock response signal with symmetrical attenuated oscillation features while the latter is employed when the measured shock records are available. A time-domain inverse system method is formulated to obtain the multi-input drive signals based on a finite-difference equation. The multi-input multi-output frequency response functions are estimated first and then used to construct the inverse system. The multi-output shock response spectra are calculated by response signals from control sensors. A closed-loop iteration control procedure is provided to correct the drive signals according to the deviations between the responses and corresponding reference values. A numerical simulation and a triaxial vibration test are carried out and the results show the feasibility and effectiveness of the proposed methods.

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Section: Introductionmentioning

confidence: 99%

Multi-shaker shock response spectra replication control

Zheng

Yan

Wei

et al. 2022

Journal of Vibration and Control

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“…The traditional MIMO Gaussian random vibration control method is no longer applicable to the non-Gaussian case (Smallwood, 1999). A challenging problem is how to generate the appropriate multi-input drive signals to make the kurtoses and power spectra of the multi-output response signals meet the corresponding reference values simultaneously (Chen et al, 2016;Zheng et al, 2020). Furthermore, the inherent physical system characteristic makes the multi-output responses at the control points coupled (Cui et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Continuous convolution and nonlinear transformation for multi-shaker non-Gaussian random vibration control

Zheng

Chen

et al. 2020

Journal of Vibration and Control

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This study proposes a continuous convolution method combined with memoryless nonlinear transformation for multi-input multi-output stationary non-Gaussian random vibration tests. The challenge of the multi-shaker non-Gaussian random vibration test lies in the coupling problems that are manifested in the inherent physical system and in the existence of cross-spectral densities. In the presented method, the independent stationary Gaussian random signals pass through a designed finite impulse response filter with a convolution manipulation first, and then the resulting signals are transformed to the non-Gaussian random signals by the memoryless nonlinear transformation method. The desired drive signals are obtained by the input–output relationship in the frequency domain. The finite impulse response filter is constructed by the frequency sampling technique in which the amplitude characteristics of the filter are determined by the predefined reference power spectral densities. A new monotonic nonlinear transformation function with an approximate kurtosis solution is provided. It only contains one parameter for kurtosis control both in sub-Gaussian and super-Gaussian cases. The memoryless nonlinear transformation is used to maintain the cross-spectral densities, although some distortions are introduced to the power spectra during the transformation process. The inverse system method is used to overcome the coupling problem caused by the inherent physical system. A simulation example and a triaxial vibration test are carried out, and the results indicate the validity and feasibility of the proposed method.

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Stationary non-Gaussian random vibration control: A review

Zheng

Chen

2021

Chinese Journal of Aeronautics

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Analysis of low damping ratios in multi-exciter stationary non-Gaussian random vibration control

Cited by 6 publications

References 15 publications

Multi-shaker shock response spectra replication control

Multi-shaker shock response spectra replication control

Continuous convolution and nonlinear transformation for multi-shaker non-Gaussian random vibration control

Stationary non-Gaussian random vibration control: A review

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