Intelligent Fault Diagnosis of Rotating Machinery Using Support vector Machine and Improved ABC

DOI

10.2991/msbda-19.2019.61How to use a DOI?

Keywords

Fault diagnosis, Local mean decomposition, Artificial bee colony, Support vector machine

Abstract

An intelligent fault diagnosis method by means of local mean decomposition (LMD), support vector machine (SVM) and improved artificial swarm (IABC) is proposed in this paper. Firstly, the vibration signals are decomposed by means of LMD method and frequency-domain and time-domain statistical characteristics of fault information are extracted. Then, a classifier model is present, which combines IABC and SVM to improve classification accuracy. Finally, SVM model identifies different fault situations adopting the optimal features and model parameters. The experiment’s result shows the effectiveness of the proposed method in fault feature extraction and fault diagnosis of rolling bearings.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

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

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TY  - CONF
AU  - Liwu Pan
AU  - Jian Xiao
AU  - Shaohua Hu
PY  - 2019/08
DA  - 2019/08
TI  - Intelligent Fault Diagnosis of Rotating Machinery Using Support vector Machine and Improved ABC
BT  - Proceedings of the 2019 International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019)
PB  - Atlantis Press
SP  - 388
EP  - 393
SN  - 2352-538X
UR  - https://doi.org/10.2991/msbda-19.2019.61
DO  - 10.2991/msbda-19.2019.61
ID  - Pan2019/08
ER  -