System reliability analysis using component-level and system-level accelerated life testing

Accelerated life testing (ALT) has been widely used to expedite the analysis of a product's reliability. Systems consisting of multiple components could be tested at component level and/or system level. Each testing level requires different resources to be performed and a specific approach to analyze the information carried with it in order to draw reliability conclusions. In addition, each of these two levels: component-level tests and system-level tests have their own advantages and disadvantages. Systems of multiple components undergoing a system-level test could be expensive, but it considers the dependence of components failure times of the system. The component-level test, consists of testing each component separately, being cheap and allowing testing customization. However, it does not include any of failure time correlations of components when assembled together in one system. This paper introduces a novel framework to analyze the reliability of systems with multiple components using ALT. The framework includes shared frailty models to model the dependence between failure time distributions of the components of a system. A Bayesian method is proposed to fuse both component-level testing information and system-level testing information to calculate system reliability by propagating and minimizing the uncertainty incurred from each testing level. Results of numerical examples show the efficacy of the proposed ALT-based system reliability analysis methods.

Automated summary

ALT is widely used to expedite product reliability analysis, but different testing levels require different resources and approaches with their own advantages and disadvantages. A novel framework is proposed using shared frailty models and Bayesian methods to combine component-level and system-level testing information for calculating system reliability effectively.