| Issue |
MATEC Web Conf.
Volume 104, 2017
2017 2nd International Conference on Mechanical, Manufacturing, Modeling and Mechatronics (IC4M 2017) – 2017 2nd International Conference on Design, Engineering and Science (ICDES 2017)
|
|
|---|---|---|
| Article Number | 01002 | |
| Number of page(s) | 6 | |
| Section | Chapter 1: Mechanical and Manufacturing Engineering | |
| DOI | https://doi.org/10.1051/matecconf/201710401002 | |
| Published online | 14 April 2017 | |
Fault condition recognition of rolling bearing in bridge crane based on PSO–KPCA
School of Mechanical and Power Engineering, North University of China, Taiyuan, 030051, China
a Corresponding author: He Yan@zbhy91@163.com
When the rolling bearing in bridge crane gets out of order and often accompanies with occurrence of nonlinear behaviours, its fault information is weak and it is difficult to extract fault features and to distinguish diverse failure modes. Kernel principal component analysis (KPCA) may realize nonlinear mapping to solve nonlinear problems. In the paper the particle swarm optimization （PSO）is applied to optimization of kernel function parameter to reduce its bind set-up. The optimal mathematical model of kernel parameters is constructed by means of thought of fisher discriminate functions .And then it is used to bridge crane rolling bearing simulated faults recognition. The simulation results show that KPCA optimized by PSO can effectively classify fault conditions of rolling bearing. It can be concluded that non-linear mapping capability of KPCA after its function parameter by PSO is greatly improved and the KPCA-PSO is very suit for slight and incipient mechanical fault condition recognition.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.