中国电机工程学报2017，Vol.37Issue(19)：5696-5706,11.DOI:10.13334/j.0258-8013.pcsee.162071
一种基于改进堆栈自动编码器的航空发电机旋转整流器故障特征提取方法
A Fault Feature Extraction Method of Aerospace Generator Rotating Rectifier Based on Improved Stacked Auto-encoder
摘要
Abstract
This paper proposed a fault feature extraction method based on the stacked auto-encoder (SAE),which is optimized by the grey relational analysis (GRA).This method can extract fault features from raw data adaptively,and this method can be applied to fault diagnosis of rotating rectifier diodes in aerospace generator.First,filed current of aerospace generator excitation is collected.Second,the deep learning theory,combined with the grey relational analysis,is adopted to train the auto-encoder for achieving a good network structure of stack auto-encoders,which can extract the fault features adaptively from the generator current data information.Finally,fault diagnosis can be implemented with the support vector machine classifier.The performances of the presented method were compared with fast Fourier transform (FFT) method through simulations and physical experiments.The experiment results showed that the presented fault extractor is automatic and adaptive,and the achieved features with this method can be evaluated ideally with the support vector machine classifier.关键词
航空发电机/旋转整流器/特征提取/自编码机/灰色关联度分析/深度学习Key words
aerospace generator/rotating rectifier/feature extraction/auto-encoder/grey relational analysis/deep learning分类
信息技术与安全科学引用本文复制引用
崔江,唐军祥,龚春英,张卓然..一种基于改进堆栈自动编码器的航空发电机旋转整流器故障特征提取方法[J].中国电机工程学报,2017,37(19):5696-5706,11.基金项目
国家自然科学基金项目(51377079) （51377079）
中央高校基本科研业务费专项资金资助(NS2017019).Project Supported by National Natural Science Foundation of China(51377079) （NS2017019）
Fundamental Research Funds for the Central Universities (NS2017019). （NS2017019）
