Model-Based Reliability Analysis With Both Model Uncertainty and Parameter Uncertainty

Abstract

Model-based reliability analysis may not be practically useful if reliability estimation contains uncontrollable errors. This paper addresses potential reliability estimation errors from model bias together with model parameters. Given three representative scenarios, reliability analysis strategies with representative methods are proposed. The pros and cons of these strategies are discussed and demonstrated using a tank storage problem based on the finite element model with different fidelity levels. It is found in this paper that the confidence-based reliability analysis considering epistemic uncertainty modeling for both model bias and model parameters can make reliability estimation errors controllable with less conservativeness compared to the direct reliability modeling using the Bayesian approach.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 11, 2019
Source ID
10.1115/1.4041946

Entities

People

  • Zhimin Xi

Organizations

  • Defense Advanced Research Projects Agency
  • National Science Foundation

Tags

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Modeling and Simulation
  • Inertial Navigation Systems.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference