Prediction of remaining life of power transformers based on left truncated and right censored lifetime data

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2009-01-01
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Meeker, William
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McCalley, James
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Center for Nondestructive Evaluation

The Center for Nondestructive Evaluation at Iowa State has been involved in the use of nondestructive evaluation testing (NDT) technologies to: assess the integrity of a substance, material or structure; assess the criticality of any flaws, and to predict the object’s remaining serviceability. NDT technologies used include ultrasonics and acoustic emissions, electromagnetic technologies, computer tomography, thermal imaging, and others.

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In October of 1985 the CNDE was approved by the State Board of Regents after it had received a grant from the National Science Foundation (NSF) as an Industry/University Cooperative Research Center.

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Prediction of the remaining life of high-voltage power transformers is an important issue for energy companies because of the need for planning maintenance and capital expenditures. Lifetime data for such transformers are complicated because transformer lifetimes can extend over many decades and transformer designs and manufacturing practices have evolved. We were asked to develop statistically-based predictions for the lifetimes of an energy company’s fleet of high-voltage transmission and distribution transformers. The company’s data records begin in 1980, providing information on installation and failure dates of transformers. Although the dataset contains many units that were installed before 1980, there is no information about units that were installed and failed before 1980. Thus, the data are left truncated and right censored. We use a parametric lifetime model to describe the lifetime distribution of individual transformers. We develop a statistical procedure, based on age-adjusted life distributions, for computing a prediction interval for remaining life for individual transformers now in service. We then extend these ideas to provide predictions and prediction intervals for the cumulative number of failures, over a range of time, for the overall fleet of transformers.

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This article is from Annals of Applied Statistics 3, no. 2 (2009): 857–879, doi:10.1214/00-AOAS231.

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Thu Jan 01 00:00:00 UTC 2009
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