Extreme learning machine based modified deep auto-encoder network classifier algorithm

Abstract

Aiming to solving the problem of slow training speed and learning efficiency existed in the deep auto-encoder network, this paper puts forward a new kind of modified deep auto-encoder network model based on extreme learning machine (ELM-MDAE). Through training the deep auto-encoder networks with the training method of extreme learning machine, the classification accuracy and training time of ELM-MDAE are compared with traditional deep auto-encoder network utilizing the rolling bearing fault vibration dataset released by Case Western Reserve University in United States. Experiments turn out to be that the average diagnostic accuracy rate could reach to 98.42%, and the average training time is 3.70 s with the method established in this paper. Therefore, ELM-MDAE possesses a better classification ability and fewer training time.