A MGF based approximation to cumulative exposure models

Watalu Yamamoto, Lu Jin

Research output: Contribution to conferencePaperpeer-review

Abstract

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.

Original languageEnglish
Pages261-268
Number of pages8
Publication statusPublished - 2016
Externally publishedYes
Event12th International Workshop on Intelligent Statistical Quality Control, IWISQC 2016 - Hamburg, Germany
Duration: 2016 Aug 162016 Aug 19

Conference

Conference12th International Workshop on Intelligent Statistical Quality Control, IWISQC 2016
Country/TerritoryGermany
CityHamburg
Period16/8/1616/8/19

Keywords

  • Accelerated lifetime model
  • Approximation
  • Cumulative exposure model
  • Moment generating function
  • Time-scale

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality

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