NDLI: Reliability prediction using support vector regression

Content Provider	Springer Nature Link
Author	Fuqing, Yuan Kumar, Uday Galar, Diego
Copyright Year	2011
Abstract	Reliability prediction of machinery is crucial to schedule overhauls, spare parts replacement and maintenance interventions. Many AI tools have been used in order to provide these predictions for the industry. Support vector regression (SVR) is a nonlinear regression technique extended from support vector machine. SVR can fit data flexibly and it has a wide variety of applications. This paper utilizes SVR combining time series to predict the next failure time based on historical failure data. To solve the parameter selection problem a method has been proposed. This method approximates the widely used leave-one-out method. To bound the prediction error, a confidence interval is proposed based on the non-homogeneous poisson process. A numerical case from the mining industry is presented to demonstrate the developed approach.
Starting Page	263
Ending Page	268
Page Count	6
File Format	PDF
ISSN	09756809
Journal	International Journal of System Assurance Engineering and Management
Volume Number	1
Issue Number	3
e-ISSN	09764348
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-04-19
Publisher Place	India
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Support vector regression Reliability prediction Parameter selection Time series Non-homogeneous poisson process Engineering Economics, Organization, Logistics, Marketing Quality Control, Reliability, Safety and Risk
Content Type	Text
Resource Type	Article
Subject	Strategy and Management Safety, Risk, Reliability and Quality

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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