计算机工程与应用2019，Vol.55Issue(20)：257-262,270,7.DOI:10.3778/j.issn.1002-8331.1806-0004
奇异值分解和稀疏自编码器的轴承故障诊断
Singular Value Decomposition and Sparse Automatic Encoder for Bearing Fault Diagnosis
摘要
Abstract
In order to solve the problem that the feature information of rolling bearing fault is difficult to be extracted under high-dimensional data and the signal fault classification needs supervised training to achieve classification, a method is proposed based on Singular Value Decomposition(SVD)and time domain feature analysis and Stacked Sparse AutoEn-coder(SAE)and Softmax classifier for classification of rolling bearing faults. This method uses Hankle matrix to recon-struct the original data, then singular value decomposition and time domain analysis is used to conduct feature preprocessing. The integrated features are used as the input of the SAE for feature optimization. The optimized features are entered into the Softmax classifier for classification recognition. The experimental results show that the recognition rate of ten types of data under three kinds of working conditions is above 96%. Compared with other methods, the recognition rate is improved. Therefore, this method can effectively perform feature preprocessing and classification of complex signals such as rolling bearings.关键词
滚动轴承故障/奇异值分解(SVD)/时域分析/堆栈稀疏自编码器(SAE)Key words
rolling bearing fault/Singular Value Decomposition(SVD)/time domain analysis/Stacked Sparse AutoEn-code(r SAE)分类
机械制造引用本文复制引用
曹浩,陈里里,司吉兵,任君兰..奇异值分解和稀疏自编码器的轴承故障诊断[J].计算机工程与应用,2019,55(20):257-262,270,7.基金项目
重庆市基础与前沿研究计划项目(No.cstc2016jcyjA0526) （No.cstc2016jcyjA0526）
重庆市教委科学技术研究项目(No.KJ1600519) （No.KJ1600519）
重庆市社会事业与民生保障科技创新专项项目(No.cstc2017shmsA30016). （No.cstc2017shmsA30016）
