NDLI: Least Squares Support Vector Machines for performance degradation modeling of, CNC equipments

Content Provider	IEEE Xplore Digital Library
Author	Yuming Xu Chao Deng Jun Wu
Copyright Year	2009
Description	Author affiliation: School of Mechanical Science and Engineering, Huazhong University of Science & Technology, Wuhan, Hubei 430074, China (Yuming Xu; Chao Deng; Jun Wu)
Abstract	Due to the applications of high technology, a catastrophic failure of Computerized numerical control (CNC) machine tool rarely happens at normal operation conditions. Traditional reliability assessment methods based on time-to-failure distributions have the difficulty to estimate the reliability precisely. This paper presents a novel reliability assessment methodology that estimates the reliability of machine tool with machining performance degradation while only small samples are required. To analyze a performance degradation process on the machine tool, Least Squares Support Vector Machines (LS-SVM) is introduced. Parameter optimization and a two-stage searching method of the parameter scope are proposed to improve the LS-SVM regression performance. A performance degradation model based on LS-SVM is built and a machining performance degradation experiment on OTM650 machine tool is used to study the feasibility of the developed technique. The results show that the proposed reliability assessment methodology is effective.
Starting Page	201
Ending Page	206
File Size	322916
Page Count	6
File Format	PDF
ISBN	9781424452187
DOI	10.1109/CYBERC.2009.5342155
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Chaos Optimization methods Machining Parameter optimization Computer numerical control Least squares methods Least Squares Support Vector Machines (LS-SVM) Support vector machines Degradation Performance degradation modeling Support vector machine classification Machine tools Performance analysis
Content Type	Text
Resource Type	Article

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