Title:
System Reliability Assessment using Covariate Theory

Thumbnail Image
Files
RAMS2004_RM111.pdf (500.72 KB)
Author(s)
Wallace, Jon Michael
 Mavris, Dimitri N.
 Schrage, Daniel P.
Authors
Advisor(s)
Advisor(s)
Editor(s)
Associated Organization(s)
Organizational Unit
Daniel Guggenheim School of Aerospace Engineering
The Daniel Guggenheim School of Aeronautics was established in 1931, with a name change in 1962 to the School of Aerospace Engineering
Organizational Unit
Organizational Unit
Series
Collections
Research Publications
Supplementary to
Permanent Link
http://hdl.handle.net/1853/6279
Abstract
A method is demonstrated that utilizes covariate theory to generate a multi-response component failure distribution as a function of pertinent operational parameters. Where traditional covariate theory uses actual measured life data, a modified approach is used herein to utilize life values generated by computer simulation models. The result is a simulation-based component life distribution function in terms of time and covariate parameters for each failure response. A multivariate joint probability covariate model is proposed by combining the covariate marginal failure distributions with the Nataf transformation approach. Evaluation of the joint probability model produced significant improvement in joint probability predictions as compared to the independent series event approach. The proposed methods are executed for a nominal aircraft engine system to demonstrate the assessment of multi-response system reliability driven by a dual mode turbine blade component failure scenario as a function of operational parameters.
Sponsor
Date Issued
2004-01
Extent
512733 bytes
Resource Type
Text
Resource Subtype
Paper
Rights Statement
Rights URI