Fault Diagnosis Based on Batch-normalized Stacked Sparse Autoencoder

Abstract

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.