Researchers using a machine known as a "shaker" to study the effects of multi-axial vibrations, 2019
Vibration fatigue is a mechanical engineering term describing material fatigue, caused by forced vibration of random nature. An excited structure responds according to its natural-dynamics modes, which results in a dynamic stress load in the material points.[1] The process of material fatigue is thus governed largely by the shape of the excitation profile and the response it produces. As the profiles of excitation and response are preferably analyzed in the frequency domain it is practical to use fatigue life evaluation methods, that can operate on the data in frequency-domain, s power spectral density (PSD).
A crucial part of a vibration fatigue analysis is the modal analysis, that exposes the natural modes and frequencies of the vibrating structure and enables accurate prediction of the local stress responses for the given excitation. Only then, when the stress responses are known, can vibration fatigue be successfully characterized.
The more classical approach of fatigue evaluation consists of cycle counting, using the rainflow algorithm and summation by means of the Palmgren-Miner linear damage hypothesis, that appropriately sums the damages of respective cycles. When the time history is not known, because the load is random (e.g. a car on a rough road or a wind driven turbine), those cycles can not be counted. Multiple time histories can be simulated for a given random process, but such procedure is cumbersome and computationally expensive.[2]
Vibration-fatigue methods offer a more effective approach, which estimates fatigue life based on moments of the PSD. This way, a value is estimated, that would otherwise be calculated with the time-domain approach. When dealing with many material nodes, experiencing different responses (e.g. a model in a FEM package), time-histories need not be simulated. It then becomes viable, with the use of vibration-fatigue methods, to calculate fatigue life in many points on the structure and successfully predict where the failure will most probably occur.
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