A BRB Based Fault Prediction Method of Complex Electromechanical Systems
Joint Authors
Wang, Zhanli
Zhang, Bangcheng
Yin, Xiaojing
Han, Xiaoxia
Gao, Zhi
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-06-08
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Fault prediction is an effective and important approach to improve the reliability and reduce the risk of accidents for complex electromechanical systems.
In order to use the quantitative information and qualitative knowledge efficiently to predict the fault, a new model is proposed on the basis of belief rule base (BRB).
Moreover, an evidential reasoning (ER) based optimal algorithm is developed to train the fault prediction model.
The screw failure in computer numerical control (CNC) milling machine servo system is taken as an example and the fault prediction results show that the proposed method can predict the behavior of the system accurately with combining qualitative knowledge and some quantitative information.
American Psychological Association (APA)
Zhang, Bangcheng& Yin, Xiaojing& Wang, Zhanli& Han, Xiaoxia& Gao, Zhi. 2015. A BRB Based Fault Prediction Method of Complex Electromechanical Systems. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1074535
Modern Language Association (MLA)
Zhang, Bangcheng…[et al.]. A BRB Based Fault Prediction Method of Complex Electromechanical Systems. Mathematical Problems in Engineering No. 2015 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1074535
American Medical Association (AMA)
Zhang, Bangcheng& Yin, Xiaojing& Wang, Zhanli& Han, Xiaoxia& Gao, Zhi. A BRB Based Fault Prediction Method of Complex Electromechanical Systems. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1074535
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1074535