Control method for multi-input multi-output non-Gaussian random vibration test with cross spectra consideration

Zheng, Ronghui; Chen, Huaihai; He, Xueming

doi:10.1016/j.cja.2017.10.001

Cited by 18 publications

(11 citation statements)

References 20 publications

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“…Figure 2 demonstrates the changes in the shape of the distribution described by kurtosis. With regard to fatigue analysis, it should be noted that it is possible to generate load curves for one PSD function profile with different values of the parameters

and

using the nonlinear transformation of the time history [ 2 ].…”

Section: Theoretical Basis and Equipmentmentioning

confidence: 99%

“…The loads of machine and device elements, which occur during typical work or operation and change over time, are usually not deterministic and can be treated as a random variable [ 1 , 2 , 3 , 4 ]. In such cases, it is reasonable to use tools from mathematical statistics and stochastics to estimate the parameters used in fatigue calculations [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

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Fatigue Test of 6082 Aluminum Alloy under Random Load with Controlled Kurtosis

Owsiński

Niesłony

2021

Materials

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This paper presents the results of experimental tests carried out on an electromagnetic shaker where the excited element was a specimen with additional weight attached to the slip table. The load was random with a different kurtosis parameter value, i.e., it was performed for non-Gaussian loads. The experiment was accompanied by basic fatigue calculations in the frequency domain and their verification with experimental results. A significant decrease in fatigue life was found to take place with an increase in kurtosis and the maintenance of the same standard deviation of the specimen load. The fatigue effect, caused by the deviation from the normal distribution that was described by the kurtosis parameter, on the fatigue life of aluminum alloy 6082 was presented. An analysis revealed the different amplitude probability distributions for the loading signal and the recorded deformation signal. It was concluded that there was a lack of sensitivity of the numerical model to the change in the kurtosis parameter of the distribution of random loads.

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and

using the nonlinear transformation of the time history [ 2 ].…”

Section: Theoretical Basis and Equipmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fatigue Test of 6082 Aluminum Alloy under Random Load with Controlled Kurtosis

Owsiński

Niesłony

2021

Materials

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“…The transformation requires an iterative search for these parameters to achieve desired kurtoses, and it might be ineffective in field test considering the number of unknown parameters. The function we used in this article was presented by Zheng et al (2017). These functions only have two unknown parameters, as shown in equation (22)where x is a Gaussian signal; b and c are unknown parameters.…”

Section: Improved Time-domain Procedures For Non-gaussian Testmentioning

confidence: 99%

“…ZMNL functions can rapidly generate time histories with different kurtosis and skewness, but they amplify the magnitude of the auto-power spectral density (ASD) and can twist its shape (Benasciutti and Tovo, 2018; Braccesi et al, 2009; Steinwolf, 2008). Zheng et al (2017) proposed a sequential phase modification method in 2017 and used the hyperbolic sine and inverse hyperbolic sine as ZMNL functions to increase and decrease kurtoses, respectively. Apparently, that method belongs to ZMNL transformation, and the researchers extracted the phase of the signals under transformation.…”

Section: Introductionmentioning

confidence: 99%

A time-domain procedure for non-Gaussian stationary environmental testing using zero-memory nonlinear transformation

Cui

Zheng

Kang

2020

Journal of Vibration and Control

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This article proposes a time-domain procedure for a non-Gaussian stationary random vibration test with prescribed power spectral densities. Previous procedures for generating non-Gaussianity suffered from certain defects. For example, zero-memory nonlinear transformation, an algorithm frequently applied to transform Gaussian signals into non-Gaussian signals, often produces changes in both auto-power spectral densities and cross-power spectral densities, which might result in control instability under certain circumstances. In this article, the authors propose a different approach for the zero-memory nonlinear function. First, a time-domain procedure for a non-Gaussian random test is introduced. Second, a rescaling method is applied to correct the magnitude amplification on the auto-power spectral density because of zero-memory nonlinear transformation. We offer experience formulas in this method to adjust the auto-power spectral density of both super-Gaussian and sub-Gaussian responses. Third, a control strategy using a finite impulse response filter is proposed to further improve the auto-power spectral density if the shape of the auto-power spectral density is distorted. The kurtosis loss induced by the filtering process is also analysed and a corresponding solution is put forward to ease the reduction. Numerical test and a biaxial shaker table test are conducted to validate the availability and superiority of the proposed procedure.

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“…Zheng and Chen etc. proposed an inverse system method combined with sequential phase modification to allow for system cross coupling characteristics and to preserve spectral structures including multi-output response kurtoses, auto spectral densities and cross spectral densities which can be controlled simultaneously and independently [17,18]. However, the reference signals should be generated frame by frame successively which is computationally intensive and time consuming.…”

Section: Introductionmentioning

confidence: 99%

Multi-exciter stationary non-Gaussian random vibration test with time domain randomization

Zheng

Vandepitte

Luo

2019

Mechanical Systems and Signal Processing

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This paper presents a new control method for multi-input multi-output stationary non-Gaussian random vibration test using time domain randomization. The control objectives are composed of response skewnesses, kurtoses and power spectral densities. The generation process of stationary and coupled reference non-Gaussian signals by specified reference skewnesses, kurtoses and spectra is analyzed. The reference non-Gaussian signals combined with system frequency response functions are then utilized to obtain the desired drive signals for dynamic inputs, in which the inverse system method in the frequency domain is employed. The primary advantages of the proposed methods are the high computational efficiency and simultaneous control of the time-frequency characteristics of response signals. In consideration of system cross coupling characteristics manifested in coherence and phase coefficients, the skewness and kurtosis tuning steps for each control channel are formulated by using a sequential phase modification method. The relationships between reference skewnesses, kurtoses and spectra are discussed and they reveal that the reference spectra have an influence on the settings of reference skewnesses and kurtoses, which implies that proper settings of reference skewnesses, kurtoses and spectra are necessary. A numerical example and a triaxial vibration test are provided and the results show the validity and feasibility of the proposed method.

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Control method for multi-input multi-output non-Gaussian random vibration test with cross spectra consideration

Cited by 18 publications

References 20 publications

Fatigue Test of 6082 Aluminum Alloy under Random Load with Controlled Kurtosis

Fatigue Test of 6082 Aluminum Alloy under Random Load with Controlled Kurtosis

A time-domain procedure for non-Gaussian stationary environmental testing using zero-memory nonlinear transformation

Multi-exciter stationary non-Gaussian random vibration test with time domain randomization

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