OS18-1 Rolling Bearings Fault Diagnosis Method Using EMD Decomposition and Probabilistic Neural Network

Title:	OS18-1 Rolling Bearings Fault Diagnosis Method Using EMD Decomposition and Probabilistic Neural Network
Publication:	ICAROB2018
Volume:	23
Pages:	691-694
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2018.OS18-1
Author(s):	Caixia Gao, Tong Wu, Ziyi Fu
Publication Date:	February 2, 2018
Keywords:	Rolling bearing, fault recognition, empirical modal decomposition, principal component analysis, probabilistic neural network
Abstract:	Aiming at the problem that the vibration signal of the early fault is weak. A fault diagnosis method of rolling bearing combined with empirical mode decomposition (EMD), principal component analysis (PCA) and probabilistic neural network (PNN) is proposed, in which the energy, kurtosis and skewness of first few IMFs are extracted as fault feature, the dimension of feature set is reduced by PCA, the new set is put into the PNN to identify fault recognition. The simulation shows that this method has higher fault diagnosis accuracy.
PDF File:	https://alife-robotics.co.jp/members2018/icarob/data/html/data/OS_pdf/OS18/OS18-1.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/