噪声与振动控制2018，Vol.38Issue(z1)：678-682,5.DOI:10.3969/j.issn.1006-1355.2018.Z1.147
基于稀疏自动编码器与FA-KELM的滚动轴承故障诊断
Rolling Bearing Fault Diagnosis based on Sparse Auto-encoder and FA-KELM
敦泊森 1柳晨曦 1王奉涛1
作者信息
- 1. 大连理工大学 振动工程研究所,辽宁 大连 116023
- 折叠
摘要
Abstract
Extracting effective rolling bearing fault feature parameters is an important part of the bearing fault diagnosis. In order to improve the high-dimensional data feature selection of kernel extreme learning aachine (KELM), a novel method of combining sparse auto-encoder (SAE) with KELM was proposed. Firstly, the vibration signal of time domain, frequency domain and time-frequency domain features were extracted to constitute a high-dimensional feature vector. Then, multi-layer SAE fusion was used to eliminate the redundancy of the features. Finally, the fused characteristics were used to train the KELM and the fault diagnosis model was obtained. According to the sensitivity of KELM to parameters, the firefly algorithm was used to optimize the parameters. To assess the validity of this method, the laboratory test data was adopt to compare the proposed method with the traditional KELM. The results show that this method has better accuracy and stability.关键词
振动与波/滚动轴承/稀疏自动编码器/核极限学习机/特征提取Key words
vibration and wave/rolling bearing/sparse auto-encoder/kernel extreme learning machine (KELM)/feature extraction分类
化学化工引用本文复制引用
敦泊森,柳晨曦,王奉涛..基于稀疏自动编码器与FA-KELM的滚动轴承故障诊断[J].噪声与振动控制,2018,38(z1):678-682,5.
