A Poisson-fault model for testing power transformers in service
type: Item
instance of: scholarly article
title: A Poisson-fault model for testing power transformers in service
zbMATH Open document ID: 1407.90136
DOI: 10.1155/2014/945258
author: Dengfu Zhao
author: Zheng Zhao
author: Qihong Duan
author: Gongnan Xie
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: This paper presents a method for assessing the instant failure rate of a power transformer under different working conditions. The method can be applied to a dataset of a power transformer under periodic inspections and maintenance. We use a Poisson-fault model to describe failures of a power transformer. When investigating a Bayes estimate of the instant failure rate under the model, we find that complexities of a classical method and a Monte Carlo simulation are unacceptable. Through establishing a new filtered estimate of Poisson process observations, we propose a quick algorithm of the Bayes estimate of the instant failure rate. The proposed algorithm is tested by simulation datasets of a power transformer. For these datasets, the proposed estimators of parameters of the model have better performance than other estimators. The simulation results reveal the suggested algorithms are quickest among three candidates.
Mathematics Subject Classification ID: 90B25
Mathematics Subject Classification ID: 62P30
Mathematics Subject Classification ID: 62N05
zbMATH DE Number: 7017676
Wikidata QID: Q59071012
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/945258
OpenAlex ID: W2059539568
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description: scientific article; zbMATH DE number 7017676