The Study on Electric Power Equipment Reliability Analysis Using Statistical Method

Ke Zhang; Yi Bo Zheng; Hang Yang Yu

doi:10.4028/www.scientific.net/AMM.203.340

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 203The Study on Electric Power Equipment Reliability...

The Study on Electric Power Equipment Reliability Analysis Using Statistical Method

Abstract:

This paper gives an overview of selected statistical methods and models commonly used in reliability engineering. The purpose of this paper is to discuss several concepts commonly applied in the analysis of equipment performance and, in particular, in predicting the remaining life of power equipment. Parametric methods are well suited for the prediction of the end of life of electric power equipment. However, care must be taken in selecting appropriate model since vastly different results can be obtained with different models fitted into the same data. Nonparametric methods are useful only when no suitable probability distribution can be fitted into the data set.

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Periodical:

Applied Mechanics and Materials (Volume 203)

Pages:

340-344

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.203.340

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Online since:

October 2012

Authors:

Ke Zhang, Yi Bo Zheng, Hang Yang Yu

Keywords:

End of Life Prediction, Mathematical Models, Reliability Analysis, Statistical Methods

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References

[1] Li W, Vaahedi E, Choudhury P. Power System Equipment Aging. IEEE Power and Energy Magazine 2010; 4(3): 52-58.

DOI: 10.1109/mpae.2006.1632454

Google Scholar

[2] Lawless JF. Statistical Models and Methods for Lifetime Data. Wiley, New York, (2010).

Google Scholar

[3] Nelson W. Applied Life Data Analysis. Wiley, (2009).

Google Scholar

[4] Wenyuan Li. Evaluating mean life of power system equipment with limited end-of-life failure data. IEEE Trans. on Power Systems 2009; 19(1): 236-242.

DOI: 10.1109/tpwrs.2003.821434

Google Scholar

[5] Anders GJ, Endrenyi J, Maciejewski H. Overview of Statistical Methods and Models and Analysis of Their Suitability For Prediction of The End of Life Of Equipment. Montreal, Quebec, Canada: Canadian Electrical Association, (2006).

Google Scholar

[6] Maciejewski H, Anders G. Problem of Model Selection for Estimation of Equipment Remaining Life. In: Proc Int Conference on Dependability of Computer Systems. DepCoS - RELCOMEX IEEE Computer Society, (2007).

DOI: 10.1109/depcos-relcomex.2007.38

Google Scholar