Multi-source fault identification based on combined deep learning

¹ Information Communications Academy, National University of Defense Technology, No.5 Guangming Road, Wangqu Town, Chang’an District, Xi’an City, 710106, China
² Faculty of School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, 710121 China
³ Xi’an Institute of Applied Optics, No.9 Dianzisan Road, Dianzicheng Town, Yanta District, Xi’an City 710102 China

^* Corresponding author: angelaxing@126.com

Abstract

This study establishes a multi-source fault identification method based on a combined deep learning strategy to identify a multi-source fault effectively in the fault diagnosis of complex industrial systems. This framework is composed of feature extraction and classifier design. In the first state, the signal is transformed to the time-frequency domain and the time-frequency feature is learned using stacked denoising autoencoders. A learning method that consists of unsupervised pre-learning and supervised fine-tuning is used to train this deep model. In the second state, a model for an ensemble multiple support vector machine classifier is created to recognize fault information. Ten types of rolling bearing signals were adopted in a simulation experiment to validate the effectiveness of the proposed framework. The results demonstrate that the joint model helps to obtain higher recognition accuracy.