A novel hierarchical training architecture for Siamese Neural Network based fault diagnosis method under small sample

Abstract

Although current deep learning-based fault diagnosis methods have made great progress, the accuracy of these models is usually attained based on many balanced training samples, which is quite difficult to meet in real industrial scenarios. In this paper, a Siamese Neural Network (SNN) based fault diagnosis method under small sample conditions is proposed and a novel hierarchical training architecture is designed to train model efficiently. Through pre-training and secondary training of the feature extractor, the SNN training stagnation problem caused by input data is addressed, which fully exploits the deep features of small sample data. Then, a classifier is added to construct the final fault diagnosis network, which improves the diagnosis speed under large test samples, and realizes accurate and efficient fault diagnosis under small sample conditions. Finally, experiments using real rotating machinery datasets show the feasibility and accuracy of the proposed method under small sample.