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  • 1.
    Xu, Fei
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering.
    Li, Chuanri
    Beihang Univ, Sch Reliabil & Syst Engn, Beijing 100191, Peoples R China..
    Jiang, Tongmin
    Beihang Univ, Sch Reliabil & Syst Engn, Beijing 100191, Peoples R China..
    Ahlin, Kjell
    Xielalin Consulting, SE-18463 Stockholm, Sweden..
    Synthesis of running RMS-induced non-Gaussian random vibration based on Weibull distribution2015In: Journal of Vibroengineering, ISSN 1392-8716, Vol. 17, no 7, p. 3662-3674Article in journal (Refereed)
    Abstract [en]

    Gaussian signal is produced by ordinary random vibration controllers to test the products in the laboratory, while the field data usually is non-Gaussian. To synthesize non-Gaussian random vibration, both the probability density function (PDF) and the damage effects must be considered. A new method is presented in this paper to synthesize non-Gaussian random vibration that is characterized by running RMS (root mean square). The essential idea is to model the non-Gaussian signal by a Gaussian signal multiplied by an amplitude modulation function (AMF). A two-parameter Weibull distribution is used to model the PDF of the running RMS and to create the AMF. The shock response spectrum (SRS) is used to detect significant shocks within the non-Gaussian signal. A case study is presented to show that the synthesized non-Gaussian signal has the same power spectral density (PSD), kurtosis, PDF and fatigue damage spectrum (FDS) as the field data.

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