Random vibration analysis of 3-Arc-Fan compliant interconnects

Chung, Philip Y.; Sitaraman, Suresh K.

doi:10.1016/j.microrel.2017.11.014

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…The ARMA method is based on linear difference equations and simple calculation, but it cannot be displayed on irregular intervals; it has the characteristics of extremely large-amplitude pulse signals; therefore, it is not completely suitable for simulating non-Gaussian random time series [14]. Vibration test is a basic technical method in modern engineering technology; it uses specific test equipment to simulate and generate products and vibration excitation during use, transportation, and storage, under laboratory conditions, and synthesize various natural vibration environments and induced vibration environments [15]. The current vibration control system can only simulate the stable random vibration environment, and the use of the stable random vibration environment to approximate the nonstable random vibration environment is easy to cause undertest or overtest and cannot meet the requirements of environment adaptability and reliability test.…”

Section: Introductionmentioning

confidence: 99%

Research on Numerical Simulation Method of Nonstationary Random Vibration Signal Sensor in Railway Transportation

Zhang¹,

Zhang²,

et al. 2022

Journal of Sensors

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During railway transportation, due to various factors such as road conditions and operating conditions and produced vibrations and shocks, this kind of vibration environment may cause fatigue damage to on-board equipment and transported goods. The authors propose a research on the numerical simulation method of the nonstationary random vibration signal sensor of railway transportation; first, they establish the mathematical model of the railway nonstationary random vibration signal sensor and then introduce the method of reconstructing the railway nonstationary random vibration signal sensor. For railway nonstationary non-Gaussian random vibration reconstruction signal, compare the time-domain characteristics of the sampled signal, and for railway nonstationary non-Gaussian random vibration reconstruction signal, compare the frequency domain characteristics of the sampled signal. The results show that the relative error of the RMSM function is within 6%, the relative error of the sliding bias function is within 10%, and the relative error of the sliding kurtosis function is within 8%. The energy distribution of the edge Hilbert amplitude spectrum is very similar, with absolute error less than 6%. The energy fluctuations are similar in each band, with absolute error rates less than 4% in most bands. The method proposed in this article, suitable for reconstruction of railway nonstationary Gaussian random vibration and nonstationary non-Gaussian vibration signal sensor, verifies the effectiveness and feasibility of the signal reconstruction method. The model and signal reconstruction method proposed in this paper are applied to the railway nonstationary Gaussian and nonstationary non-Gaussian random vibration sampling signals.

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Section: Introductionmentioning

confidence: 99%