Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings
Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings
复制标题
滚动轴承故障诊断的ELM复合特征选择与参数优化
DOI：
10.1016/j.isatra.2016.08.022
复制
发表时间：
2016-11-01
期刊：
影响因子：
7.3
通讯作者：
An, Xueli
中科院分区：
文献类型：
--
作者：
Luo, Meng;Li, Chaoshun;An, Xueli
本文提出了一种用于滚动轴承故障诊断的HGSA-ELM混合系统，该系统采用实值重力搜索算法（RGSA）对ELM的输入权值和偏置进行优化，并利用GSA的二值值（BGSA）从复合特征集中选择重要特征。采用集成经验模态分解（EEMD）方法，提取时间频率特征、能量特征和奇异值特征三种故障特征组成复合特征集。针对典型滚动体轴承系统56种工况的故障诊断，设计了对比实验对该方法进行了验证。结果表明，HGSA-ELM的分类准确率明显高于其原始版本和文献方法。(c) 2016年。Elsevier Ltd.出版。版权所有。
This paper proposes a hybrid system named as HGSA-ELM for fault diagnosis of rolling element bearings, in which real-valued gravitational search algorithm (RGSA) is employed to optimize the input weights and bias of ELM, and the binary-valued of GSA (BGSA) is used to select important features from a compound feature set. Three types fault features, namely time and frequency features, energy features and singular value features, are extracted to compose the compound feature set by applying ensemble empirical mode decomposition (EEMD). For fault diagnosis of a typical rolling element bearing system with 56 working condition, comparative experiments were designed to evaluate the proposed method. And results show that HGSA-ELM achieves significant high classification accuracy compared with its original version and methods in literatures. (C) 2016 ISA. Published by Elsevier Ltd. All rights reserved.