噪声与振动控制2019，Vol.39Issue(5)：197-202,249,7.DOI:10.3969/j.issn.1006-1355.2019.05.037
一种基于稀疏自编码器的电机故障诊断方法
A Motor Fault Diagnosis Method based on Sparse Autoencoders
摘要
Abstract
In the field of motor fault diagnosis, the fault diagnosis based on artificial intelligence technology and modern signal processing methods is gradually becoming a research hotspot. General pattern recognition methods usually have high requirements for data acquisition and signals processing, and are constrained by the limited generalization ability of models. In order to solve this problem, a fault diagnosis method based on sparse autoencoder is proposed. Hilbert envelope spectrum signal is used to train the sparse autoencoder. The intrinsic features of large data are adaptively refined into simple feature functions, and the intelligent diagnosis of motor status is realized through the expression of feature functions. The experimental results show that compared with back propagation (BP) neural network and support vector machine classification algorithm, this method can improve the accuracy of fault classification quickly and effectively, and is of great significance for accurate diagnosis of motor faults.关键词
故障诊断/稀疏自编码/模式识别/特征提取Key words
fault diagnosis/sparse autoencoder/pattern recognition/feature extraction分类
信息技术与安全科学引用本文复制引用
王黎阳,杜翀,汪欣,翟旭平..一种基于稀疏自编码器的电机故障诊断方法[J].噪声与振动控制,2019,39(5):197-202,249,7.基金项目
中国科学院重点部署资助项目(KFZD-SW-310) （KFZD-SW-310）
