NDLI: Condition Residual Life Evaluation by Support Vector Machine

Content Provider	IEEE Xplore Digital Library
Author	Chen Li Li Tao Bai Yongsheng
Copyright Year	2007
Description	Author affiliation: Ordinance Eng. Coll., Shijiazhuang (Chen Li; Bai Yongsheng)
Abstract	It is more and more important to predict the condition residual life according to the condition information of the equipment in order to make scientific and exact maintenance decision in modern production and defense construction. Currently the adopted models in the on-condition maintenance field are proportional hazards model (PHM) and filtering model. Both of them have complex forms and complex calculation process, the estimation of parameters is established on an amount of sample. However support vector machine (SVM) is a new machine learning method. It has the characters of simple structure, excellent learning capability and fitting in small sample. It also can transfer the problem to the square regression problem. So it can get the best resolution in the public area. SVM is extended to the application of the regression estimation of system. Therefore, SVM is adopted in the condition residual life regression. And the algorithm of the realizing this method is proposed. Finally the predicted result of the example adopted SVM show that SVM can better solve the same kind of problem.
File Size	511467
File Format	PDF
ISBN	9781424411351
DOI	10.1109/ICEMI.2007.4351178
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Filtering Instruments Predictive models Educational institutions Hazards Condition residual life Prognostics and health management condition information Support vector machines Learning systems Condition monitoring support vector machine Cathode ray tubes prediction
Content Type	Text
Resource Type	Article

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