Joint pairwise graph embedded sparse deep belief network for fault diagnosis

Abstract

An enhanced intelligent diagnosis method is proposed based on a joint pairwise graph embedded sparse deep belief network with partial least square fine-tuning (J-PDBN). In this novel framework, the joint pairwise graph embedded sparse deep belief network (DBN) is considered as an unsupervised learning method to realize fast parameters initialization and to extract data features. It combines the advantages of both the pairwise graph and sparse representation, which can preserve the manifold structure of the original data and generate discriminant features. The partial least square (PLS) is used to optimize the parameters to eliminate the gradient diffusion in the supervised learning process. The J-PDBN-based fault diagnosis is implemented by both the unsupervised learning method and PLS fine-tuning, which contributes to better classification capabilities. Finally, gearbox and bearing fault diagnosis experiments are conducted. The results show that the total recognition rates of the proposed method are 93.65% in the gearbox case and 95.96% in the bearing case, which are higher than those of other methods. Specifically, the testing accuracy is approximately 10% higher than those of the DBN network for both cases. This validates the effectiveness and superiority of the proposed method.