Advanced Rolling Bearing Fault Diagnosis Using Ensemble Empirical Mode Decomposition, Principal Component Analysis and Probabilistic Neural Network

DOI

10.2991/jrnal.2018.5.1.3How to use a DOI?

Keywords

Rolling bearing; fault recognition; ensemble empirical modal decomposition; principal component analysis; probabilistic neural network

Abstract

Aiming at the problem that the vibration signal of the incipient fault is weak, an automatic and intelligent fault diagnosis algorithm combined with ensemble empirical mode decomposition (EEMD), principal component analysis (PCA) and probabilistic neural network (PNN) is proposed for rolling bearing in this paper. EEMD is applied to decompose the vibration signal into a sum of several intrinsic mode function components (IMFs), which represents the signal characteristics of different scales. The energy, kurtosis and skewness of first few IMFs are extracted as fault feature index. PCA is employed to the fault features as the linear transform for dimension reduction and elimination of linear dependence between the fault features. PNN is applied to detect rolling bearing occurrence and recognize its type. The simulation shows that this method has higher fault diagnosis accuracy.

Copyright

Copyright © 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Caixia Gao
AU  - Tong Wu
AU  - Ziyi Fu
PY  - 2018
DA  - 2018/06/30
TI  - Advanced Rolling Bearing Fault Diagnosis Using Ensemble Empirical Mode Decomposition, Principal Component Analysis and Probabilistic Neural Network
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 10
EP  - 14
VL  - 5
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2018.5.1.3
DO  - 10.2991/jrnal.2018.5.1.3
ID  - Gao2018
ER  -