An Adaptive Support Vector Regression Machine for the State Prognosis of Mechanical Systems
Joint Authors
Liu, Fei
Zhang, Qing
Wan, Xiang
Xu, Guanghua
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-21
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Due to the unsteady state evolution of mechanical systems, the time series of state indicators exhibits volatile behavior and staged characteristics.
To model hidden trends and predict deterioration failure utilizing volatile state indicators, an adaptive support vector regression (ASVR) machine is proposed.
In ASVR, the width of an error-insensitive tube, which is a constant in the traditional support vector regression, is set as a variable determined by the transient distribution boundary of local regions in the training time series.
Thus, the localized regions are obtained using a sliding time window, and their boundaries are defined by a robust measure known as the truncated range.
Utilizing an adaptive error-insensitive tube, a stabilized tolerance level for noise is achieved, whether the time series occurs in low-volatility regions or in high-volatility regions.
The proposed method is evaluated by vibrational data measured on descaling pumps.
The results show that ASVR is capable of capturing the local trends of the volatile time series of state indicators and is superior to the standard support vector regression for state prediction.
American Psychological Association (APA)
Zhang, Qing& Liu, Fei& Wan, Xiang& Xu, Guanghua. 2015. An Adaptive Support Vector Regression Machine for the State Prognosis of Mechanical Systems. Shock and Vibration،Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1078167
Modern Language Association (MLA)
Zhang, Qing…[et al.]. An Adaptive Support Vector Regression Machine for the State Prognosis of Mechanical Systems. Shock and Vibration No. 2015 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1078167
American Medical Association (AMA)
Zhang, Qing& Liu, Fei& Wan, Xiang& Xu, Guanghua. An Adaptive Support Vector Regression Machine for the State Prognosis of Mechanical Systems. Shock and Vibration. 2015. Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1078167
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1078167