An Improved Parameter Estimation Method for Three-Parameter Weibull Distribution in the Life Analysis of Rolling Bearing

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Three-parameter Weibull distribution is playing a more and more important role in the reliability analysis of mechanical products. It can provide higher accuracy and better reflection of reliability in operating situation concerning fitting and parameter estimation for the rolling bearing life data. This paper focuses on the theory derivation of the maximum likelihood estimation of the three-parameter Weibull distribution, puts forward an improved method for the model parameter estimation and draws the life distribution model. Following that, the method has been proved to be correct and accurate by practical examples.The proposed method can provide a more accurate estimate way for the life analysis of rolling bearing based on three-parameter Weibull distribution.

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442-446

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September 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Chen Xun, Tao JunYong, Zhang ChunHua, Jiang Yu. Mechatronic System Reliability Engineering [M]. BeiJing:Science Press,(2010).

Google Scholar

[2] Yan X D, Ma X, Zheng R Y, et al. Comparison of the Parameters Estimation Methods for 3-Parameter Weibull Distribution, Jounal of NingBo University (Nature Scirnce edition). 2005, 18(3): 301-305.

Google Scholar

[3] Deng J, Gu D S, Li X B. Parameters and Quantile Estimation for Fatigue Life Distribution Using Probability Weigthed Moments. Chinese Journal of Computional Mechanics. 2004, 21(5): 609-613.

Google Scholar

[4] Fu H M, Gao Z T. An Optimization Method of Correlation Coefficient for Determining a Three-parameter Weibull Distribution. Acta Aeronautica et Astronautica Sinica. 1990, 11(7): A323-A327.

Google Scholar

[5] Zheng R Y, Yan J S. New Estimation Method of Three-parameter Weibull Distribution. Journal of Mechanical Strength. 2002. 24(4): 599-601.

Google Scholar

[6] Howard Rockette, Charles Antle, Klimko L A. Maximum Likelihood Estimation with the Weibull Model. Journal of the American Statistical Association March, 1974, 169(345): 246-249.

DOI: 10.1080/01621459.1974.10480164

Google Scholar

[7] Qiao H Z, Tsokos C P.Estimation of the three parameter weibull probability distribution[J].Mathematics and Computers in Simulation,1995,39(1-2):173-185.

DOI: 10.1016/0378-4754(95)95213-5

Google Scholar

[8] Wang H S, Li Z H, Lin R W. Maximum Likelihood Estimation for Three-parameter Weibull Distribution Based on Wear out Fault. China Railway Science. 2004, 25(5): 39-42.

Google Scholar

[9] Yang M C, Nie H. Advanced Algorithm for Maximum Likelihood Estimation of Three- Parameter Weibull Distribution. Journal of Nanjing University of Aeronautics & Astronautics. 2007, 39(1): 22-25.

Google Scholar

[10] Yang X W. Study On Three Parameters Weibull Distribution of the Rolling Bearing Fatigue Life, (2003).

Google Scholar

[11] Ahmad K E.Modified weighted least-squares estimators for the three-parameter Weibull distribution[J].Applied Mathematics Letters,1994,7(5):53-56.

DOI: 10.1016/0893-9659(94)90072-8

Google Scholar

[12] Shi J Z, Ren X J, Chen X C. A Solution Method for 3-Parameter Weibull Distribution of Maximum Likelihood Estimation. Henan Science. 2009, 27(7): 832-834.

Google Scholar

[13] Fang ZhiQiang, Gao lianHua. Estimation of Parameters of Three-parameter Weibull Distribution in Life Analysis. Journal of Armored Force Engineering Institute. 1999, 1 3(1) : 70-74.

Google Scholar