A Novel Multimode Fault Classification Method Based on Deep Learning
Joint Authors
Zhou, Funa
Gao, Yulin
Wen, Chenglin
Source
Journal of Control Science and Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-03-20
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Electronic engineering
Information Technology and Computer Science
Abstract EN
Due to the problem of load varying or environment changing, machinery equipment often operates in multimode.
The data feature involved in the observation often varies with mode changing.
Mode partition is a fundamental step before fault classification.
This paper proposes a multimode classification method based on deep learning by constructing a hierarchical DNN model with the first hierarchy specially devised for the purpose of mode partition.
In the second hierarchy , different DNN classification models are constructed for each mode to get more accurate fault classification result.
For the purpose of providing helpful information for predictive maintenance, an additional DNN is constructed in the third hierarchy to further classify a certain fault in a given mode into several classes with different fault severity.
The application to multimode fault classification of rolling bearing fault shows the effectiveness of the proposed method.
American Psychological Association (APA)
Zhou, Funa& Gao, Yulin& Wen, Chenglin. 2017. A Novel Multimode Fault Classification Method Based on Deep Learning. Journal of Control Science and Engineering،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1173433
Modern Language Association (MLA)
Zhou, Funa…[et al.]. A Novel Multimode Fault Classification Method Based on Deep Learning. Journal of Control Science and Engineering No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1173433
American Medical Association (AMA)
Zhou, Funa& Gao, Yulin& Wen, Chenglin. A Novel Multimode Fault Classification Method Based on Deep Learning. Journal of Control Science and Engineering. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1173433
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1173433