Reliability Assessment of Numerical Control Machine Tools Using Weibull Mixture Models

Zhi Ming Wang; Jian Guo Yang

doi:10.4028/www.scientific.net/AMR.181-182.161

Paper Titles

Human Facial Feature Point Detection is Based on Improved ASM Method
p.139

Reliable Η_∞ Control for Discrete-Time Switched Linear Systems with Intermittent Measurements
p.145

Geriatric Care in Healthcare Information System
p.151

Study and Practice of Recrystallization Annealing Process of High-Plasticity Cold-Rolled Steel Sheet
p.156

Reliability Assessment of Numerical Control Machine Tools Using Weibull Mixture Models
p.161

Application of Least Square-Support Vector Machines in Reliability Analysis of NC Machine Tools
p.166

Application of Association Rule in Students’ Results in University
p.172

A Novel Control Procedure for Single Axis Roll Forming
p.177

The Magnetic Field Analysis of Dual-Coil Self-Sensing Actuator
p.183

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 181-182Reliability Assessment of Numerical Control...

Reliability Assessment of Numerical Control Machine Tools Using Weibull Mixture Models

Abstract:

Weibull distribution is one of the most widely used model in reliability analysis of NC machine tools. In order to assess the reliability of NC machine tool, Generalized Expectation Maximization (GEM) algorithm is used to estimate parameters of mixture Weibull models. Akaike information criterion (AIC) and Bayesian information criterion (BIC) are used as comprehensive criteria to select the number of subpopulations of mixture models. The results show that mixture models are more suitable to assess reliability of NC machine tools than that of single model. Root mean square errors (RMSE) of mixture models reduce by 87.5% than that of single model. Reliability evaluation results of machine tool, such as MTBF etc., are given.

You might also be interested in these eBooks

Advanced Materials Science and Technology, ICMST 2010

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 181-182)

Pages:

161-165

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.181-182.161

Citation:

Cite this paper

Online since:

January 2011

Authors:

Zhi Ming Wang, Jian Guo Yang

Keywords:

Generalized Expectation Maximization (GEM), Maximum Likelihood-Estimation (MLE), Reliability Assessment, Weibull Mixture Models

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Y. Wang, Y. Jia and J. Yu, et al.: Failure probabilistic model of CNC lathes. Reliability Engineering and System Safety, Vol. 65 (1999), pp.307-314.

DOI: 10.1016/s0951-8320(98)00102-1

Google Scholar

[2] D. N. P. Murthy, M. Bulmer and J. A. Eccleston: Weibull model selection for reliability modeling. Reliability Engineering and System Safety, Vol. 86 (2004), 257-267.

DOI: 10.1016/j.ress.2004.01.014

Google Scholar

[3] D. Dai, Y. Zhou and Y. Jia: Distribution of time between failures of machining center based on type I censored data. Reliability Engineering and System Safety, Vol. 79 (2003), pp.377-379.

DOI: 10.1016/s0951-8320(02)00243-0

Google Scholar

[4] A. C. Cohen: Maximum likelihood estimation in the Weibull distribution based on complete and on censored samples. Technometrics, Vol. 7 (1965), pp.579-588.

DOI: 10.1080/00401706.1965.10490300

Google Scholar

[5] T. Bucar, M. Nagode and M. Fajdiga: Reliability approximation using finite Weibull mixture distributions. Reliability Engineering and System Safety, Vol. 84 (2004), pp.241-251.

DOI: 10.1016/j.ress.2003.11.008

Google Scholar

[6] K. P. Burnham and D. R. Anderson: Multimodel Inference: Understanding AIC and BIC in Model Selection. Sociological Methods and Research, Vol. 33 (2004), pp.261-304.

DOI: 10.1177/0049124104268644

Google Scholar