A Sequential Accelerated Life Testing Framework for System Reliability Assessment With Untestable Components

Abstract

Testing of components at higher-than-nominal stress level provides an effective way of reducing the required testing effort for system reliability assessment. Due to various reasons, not all components are directly testable in practice. The missing information of untestable components poses significant challenges to the accurate evaluation of system reliability. This paper proposes a sequential accelerated life testing (SALT) design framework for system reliability assessment of systems with untestable components. In the proposed framework, system-level tests are employed in conjunction with component-level tests to effectively reduce the uncertainty in the system reliability evaluation. To minimize the number of system-level tests, which are much more expensive than the component-level tests, the accelerated life testing (ALT) design is performed sequentially. In each design cycle, testing resources are allocated to component-level or system-level tests according to the uncertainty analysis from system reliability evaluation. The component-level or system-level testing information obtained from the optimized testing plans is then aggregated to obtain the overall system reliability estimate using Bayesian methods. The aggregation of component-level and system-level testing information allows for an effective uncertainty reduction in the system reliability evaluation. Results of two numerical examples demonstrate the effectiveness of the proposed method.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jul 24, 2018
Source ID
10.1115/1.4040626

Entities

People

  • Zhen Hu
  • Zissimos P. Mourelatos

Organizations

  • Oakland University
  • United States Army Tank Automotive Research, Development and Engineering Center
  • University of Michigan–Dearborn

Tags

Fields of Study

  • Engineering

Readers

  • Aerospace Test and Evaluation
  • Computational Modeling and Simulation
  • Software Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference