A MGF based approximation to cumulative exposure models

研究成果: Paper査読

抄録

Online monitoring data contains various measurements of the activity of the system. The amounts of works are also measured in various ways. When we model the reliability of a system, the intensity or the risk of failure events, we need to choose a time scale. Though there should be genuine time scales for each failure phenomenon, the field data including online monitoring data may not be able to provide evidence for them. There are many uncontrollable factors in the field. Many variables are monotone increasing and highly correlated with each other within a system. Yet they also represent the differences among systems. This article tries to build a bridge between two useful approaches, alternative time scale ( Kordonsky and Gertsbakh (1997), Duchesne and Lawless (2002)) and cumulative exposure model ( Hong and Meeker (2013)), by assuming the stationarity of the increments of these measurements within a system.

本文言語English
ページ261-268
ページ数8
出版ステータスPublished - 2016
外部発表はい
イベント12th International Workshop on Intelligent Statistical Quality Control, IWISQC 2016 - Hamburg, Germany
継続期間: 2016 8月 162016 8月 19

Conference

Conference12th International Workshop on Intelligent Statistical Quality Control, IWISQC 2016
国/地域Germany
CityHamburg
Period16/8/1616/8/19

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 安全性、リスク、信頼性、品質管理

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