Research on early fault diagnosis for rolling bearing based on permutation entropy algorithm

Abstract

Permutation Entropy (PE) is a new subject which talks about the scrambling and non-linearity of complex system, which has been widely studied in recent years. This paper aims to introduce the basic algorithm of PE firstly, then verify PE using simulated signal, which shows that PE is feasible for fault diagnosis. Finally, the whole life vibration data of a rolling bearing is taken as an example, comparing with variation characteristics of mean square value obtained from the vibration signal, it is well proved that the early abnormity character of vibration signal could be successfully detected by the PE early before the fault occurred. The algorithm of PE was very simple and effective for early fault diagnosis, which supported the feasible idea for the on-line fault diagnosis, so this method will play a key role in the predictive maintenance.