Study on Mechanical Concurrent Fault Diagnosis Method Based on PSO-MRLSSVM

DOI

10.2991/mmsa-18.2018.39How to use a DOI?

Keywords

mechanical faults; concurrent fault diagnosis; Particle Swarm Optimization (PSO); Multiple Regression Least Squares Support Vector Machines (Multi-regression LSSVM or MRLSSVM); parameter optimization

Abstract

In view of those characteristics such as nonlinearity and high dimensional features for mechanical concurrent faults and any conventional classifier being not able to fit multi-output needs, a concurrent fault classification method based on PSO-MRLSSVM was put forward, where the simple and fast searching ability of PSO may be utilized to optimize the penalty and Kernel parameters for the MRLSSVM algorithm; thus, the aimlessness (manually specifying parameters) may be prevented and the prediction precision of MRLSSVM may be improved. Experimental results indicate that our mechanical concurrent fault diagnosis model based on PSO-MRLSSVMs works well for effective identification of concurrent fault types and diagnosis effects are good.

Copyright

© 2018, 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  - Hongtao Wang
AU  - Lin Ye
AU  - Chun Yan
AU  - Hao Pan
PY  - 2018/03
DA  - 2018/03
TI  - Study on Mechanical Concurrent Fault Diagnosis Method Based on PSO-MRLSSVM
BT  - Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018)
PB  - Atlantis Press
SP  - 177
EP  - 181
SN  - 1951-6851
UR  - https://doi.org/10.2991/mmsa-18.2018.39
DO  - 10.2991/mmsa-18.2018.39
ID  - Wang2018/03
ER  -