Application of ARMA modelling and alpha-stable distribution for local damage detection in bearings
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Diagnostics and Vibro-Acoustics Science Laboratory, Wroclaw University of Technology, Na Grobli 15, 50-421 Wroclaw, Poland,
Hugo Steinhaus Center, Institute of Mathematics and Computer Science, Wroclaw University of Technology, Janiszewskiego 14a, 50-370 Wroclaw, Poland
Publication date: 2017-12-20
Diagnostyka 2014;15(3):3–10
In this paper a novel method for informative frequency band selection is presented. It is suitable for a vibration signal from a damaged rotating machine which is consisted of a pulse train, but it might be contaminated by other vibrations, often with higher energy. We first decompose the signal into simpler sub-signals and analyze those sub-signals using statistical tools, i.e. autoregressive moving average modelling and fitting of the α -stable distribution. The choice of this distribution is motivated by its excellent ability of modeling heavy-tailed data, i.e impulsive data. We illustrate the proposed methodology by analysis of real vibration signals from heavy-duty rotating machinery. The results prove that this statistical analysis is very efficient in informative frequency band selection in presence of high-energy contamination