MATEC Web Conf.
Volume 309, 20202019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
|Number of page(s)||11|
|Section||Smart Algorithms and Recognition|
|Published online||04 March 2020|
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: firstname.lastname@example.org
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.
Key words: Multi-source fault / Stacked denoising autoencoder / Multiple classifier combination / Deep learning
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.