Mechanics & Industry
Volume 18, Number 8, 2017
Experimental Vibration Analysis
|Number of page(s)||12|
|Published online||05 September 2018|
Characterization of random Gaussian and non-Gaussian stress processes in terms of vibration fatigue
11 Allée des Marronniers,
Versailles Cedex, France
* e-mail: email@example.com
Accepted: 30 November 2017
In the field of military land vehicles, random vibration processes generated by all-terrain wheeled vehicles in motion are not classical stochastic processes of a stationary and Gaussian nature. The non-Gaussian nature of the processes is expressed in particular by very significant flattening levels that can affect the fatigue design of mechanical structures, conventionally acquired by spectral approaches, based essentially on spectral moments of stress processes. Due to these technical considerations, techniques for the characterization of random excitation processes generated by this type of driving situation need to be developed, by proposing innovative characterization methods no longer based on deterministic spectral and/or temporal approaches but on temporal approaches of a stochastic nature. Indeed, to characterize the fatigue damage produced by non-stationary and non-Gaussian random processes, the author shows that it is now necessary to mix time-counting techniques used in the field of vibration fatigue with those of the sampling statistics used in estimation theory. This approach makes it possible to extrapolate favorably over time the level of damage to structures, from a statistical perspective, when this extrapolation phase is in practice carried out deterministically. This technique, referred to as the disjoint block method (BDM), has been tested successfully in the context of component specification techniques from the reliability standpoint since 2010, and just recently integrated AFNOR standards.
Key words: Random vibration / non-stationary and non-Gaussian random processes / disjoint block method (BDM) / spectral XRS and XFS approach / temporal SRX and SFX approach
© AFM, EDP Sciences 2018
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