Computing the cdf for degrading dynamic systems

G. J. Savage, Y. K. Son, T. S. Seecharan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The times and frequencies of inspection, maintenance and replacement in degrading dynamic systems are difficult to determine. Mechanistic computer models are helpful but are inefficient because its complexity and the uncertainties in system characteristics and degradation rates. Probability distributions that are traditionally calculated through Monte Carlo Methods require thousands and thousands of time consuming lifetime simulations, rendering the creation of the cumulative distribution of time to failure onerous. The paper presents a novel methodology that 1) replaces the implicit mechanistic model with a simple explicit model, 2) transforms the dynamic, probabilistic, problem into a time invariant probability problem over each cycle-time, and 3), builds the cumulative distribution function (Cdf) as the summation of the incremental service-time failure probabilities over the planned service time. Error analysis suggests ways to predict and minimize errors. A Case Study of a servo-control mechanism shows how the new methodology builds a Cdf and yet provides controllable accuracy and a substantial time reduction when compared to Monte Carlo sampling with the traditional mechanistic model.

Original languageEnglish (US)
Title of host publicationProceedings - 18th ISSAT International Conference on Reliability and Quality in Design
Pages345-349
Number of pages5
StatePublished - Dec 1 2012
Event18th ISSAT International Conference on Reliability and Quality in Design - Boston, MA, United States
Duration: Jul 26 2012Jul 28 2012

Publication series

NameProceedings - 18th ISSAT International Conference on Reliability and Quality in Design

Other

Other18th ISSAT International Conference on Reliability and Quality in Design
CountryUnited States
CityBoston, MA
Period7/26/127/28/12

Keywords

  • Dynamic Systems
  • First-Order Reliability Method
  • Metamodels
  • Random Variable Degradation
  • Set-Theory
  • Time-Variant Reliability

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