Fault Diagnosis Based on Batch-normalized Stacked Sparse Autoencoder
作者单位：College of Information Science and Engineering Northeastern University
会议名称：《第三十九届中国控制会议》
会议日期：2020年
学科分类：08[工学] 081104[工学-模式识别与智能系统] 0804[工学-仪器科学与技术] 0802[工学-机械工程] 0811[工学-控制科学与工程] 080201[工学-机械制造及其自动化]
关 键 词：Fault diagnosis Sparse autoencoder Batch-normalization
摘 要：A fault diagnosis method based on batch-normalization stacked sparse autoencoder（SSAE） is presented in this paper. This paper use the autoencoder to extract features for fault diagnosis on account of its good performance in feature extraction. In order to improve the accuracy of the extracted features, this paper use a sparse representation which is a constraint during the encoding process. The multi-layer structure of autoencoder has an internal covariate shift problem and the generalization ability of the network is critical, batch normalization is employed before the activation function in each layer of the autoencoder network. And a stacked method is utilized to optimize network structure and reduce training difficulty. So the features of the original signal are extracted by the network based on the above method and the extracted features are placed in the classifier to identify different health states. For the purpose of fault diagnosis,this paper uses the proposed method to experiment with the bearing data set provided by Case Western Reserve University（CWRU）. The experiment proves that the proposed method has a better fault diagnosis performance compared with other traditional methods.