基于自适应邻域局部保留ELM-AE的机械故障诊断
MECHANICAL FAULT DIAGNOSIS BASED ON ADAPTIVE NEIGHBORHOOD PRESERVING ELM-AE
张焕可 1王帅旗 1陈会涛2
作者信息
- 1. 许昌电气职业学院机电工程系 河南许昌 461000
- 2. 河南理工大学机械与动力工程学院 河南焦作 454003
- 折叠
摘要
针对机器学习故障诊断中存在的先验知识依赖以及数据利用不充分问题,提出一种自适应邻域的局部保留极限学习机自动编码器方法.成对样本在原始数据空间和嵌入的表示空间中引入欧几里得距离惩罚因子,实现数据样本的相似性分类;提出一个统一的目标函数,可以同时学习数据表示和关联矩阵,并提出一个软判别约束防止过度拟合.实验结果表明,融合学习关联矩阵和数据表示方法具有学习速度快、泛化能力强和诊断精度高等优点.
Abstract
In order to solve the problems of prior knowledge dependence and insufficient data mining in machine learning fault diagnosis,a local preserving extreme learning machine automatic encoder based on adaptive neighborhood is proposed.Euclidean distance penalty factor was introduced into the original data space and the embedded representation space for paired samples to realize the similarity classification of data samples.A unified objective function was proposed,which could simultaneously learn data representation and correlation matrix,and a soft discriminative constraint was proposed to prevent overfitting.The experimental results show that the fusion learning association matrix and data representation method has the advantages of fast learning speed,strong generalization ability and high diagnostic accuracy.
关键词
极限学习机/自动编码器/关联矩阵学习/自适应邻域/机器故障诊断Key words
Extreme learning machine/Automatic encoder/Affinity learning matrix/Adaptive neighborhood/Machine fault diagnosis引用本文复制引用
基金项目
2018年度河南省重点研发与推广专项(182102310793)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据