Fault Diagnosis Based on A Stacked Sparse Auto-Encoder Network and KNN Classifier

Aiming at the problems of rolling bearings fault diagnosis, this paper proposes a hybrid intelligent fault diagnostic model which combines stacked sparse auto-encoding network (SSAE) and KNN classifier. As a branch of deep learning, SSAE has excellent ability of feature learning, which is able to reduce the dimension of frequency domain signal and extract deep features. With the help of sparse constraint, a penalty term is added to strengthen its generalization ability. Combined with the advantages of K-Nearest Neighbor algorithm (KNN) in dealing with multi-classification problems, the accuracy of the proposed model can be improved. In addition, for the purpose of validating the feasibility and superiority of the method, extensive contrast experiments are carried out. Experimental results show that compared to traditional methods, making use of deep neural network for self-extraction frequency-domain features not only avoids excessive dependence on professional knowledge and project experience, but also improves the accuracy of fault classification.