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A New Approach to Testing the Distribution Type of Life Data

Journal Name:

  • International Journal of Science and Engineering Investigations

Publication Year:

  • 2017

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
In order to calculate reliability indices of some important equipment, the life data type of equipment or product needs to be ascertained firstly. Aiming to this question, the linear regression and correlation coefficient method is suggested. The linearization of common distribution like as exponential distribution is complied with the principle of linear regression. But for three-parameter Weibull distribution, the location parameter usually cannot be ascertained. A binary search algorithm is programmed to locate the location parameter firstly. Then through the comparison of correlation coefficient, the most fitted distribution type can be found out quickly. The result also shows that the inferred distribution type is in accord with the facts. Example illustrates the effectiveness and validness of linear regression and correlation coefficient for the inference of distribution type of life data.
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REFERENCES

References: 

[1] Reschenhofer, E., Generalization of the Kolmogorov-Smirnov test. Computational Statistics and Data Analysis, 24(4):1997, 433-441.
[2] Andrade, F.A., I. Esat, and M.N.M. Badi, A new approach to time-domain vibration condition monitoring: Gear tooth fatigue crack detection and identification by the Kolmogorov-Smirnov test. Journal of Sound and Vibration, 240(5):2001, 909-919.
[3] Drezner, Z., O. Turel, and D. Zerom, A modified kolmogorov-smirnov test for normality. Communications in Statistics: Simulation and Computation, 39(4):2010, 693-704.
[4] Hauschild, T. and M. Jentschel, Comparison of maximum likelihood estimation and chi-square statistics applied to counting experiments. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 457(1-2):2001, 384-401.
[5] Chen, Y.-T. and M.C. Chen, Using chi-square statistics to measure similarities for text categorization. Expert Systems with Applications, 38(4):2011, 3085-3090.
[6] Fothergill, J.C., Estimating the cumulative probability of failure data points to be plotted on Weibull and other probability paper. IEEE transactions on electrical insulation, 25(3):1990, 489-492.
[7] Takahashi, T., Statistical inference by normal probability paper. Computers and Industrial Engineering, 37(1):1999, 121-124.
[8] Jen-Tzung, C., Quasi-Bayes linear regression for sequential learning of hidden Markov models. IEEE Transactions on Speech and Audio Processing, 10(5):2002, 268-278.
[9] Jen-Tzung, C., Linear regression based Bayesian predictive classification for speech recognition. Speech and Audio Processing, IEEE Transactions on, 11(1):2003, 70-79.
[10] Cacciari, M., G. Mazzanti, and G.C. Montanari, Comparison of maximum likelihood unbiasing methods for the estimation of the Weibull parameters. IEEE Transactions on Dielectrics and Electrical Insulation, 3(1):1996, 18-27.
[11] Hirose, H., Maximum likelihood estimation in the 3-parameter Weibull distribution. A look through the generalized extreme-value distribution. Dielectrics and Electrical Insulation, IEEE Transactions on, 3(1):1996, 43-55.
[12] Zhang, L.F., M. Xie, and L.C. Tang, Bias correction for the least squares estimator of Weibull shape parameter with complete and censored data. Reliability Engineering and System Safety, 91(8):2006, 930-939.

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