Multi-Level Information Aggregation for Reliability Assurance of Hierarchical Systems

Abstract

Reliability assurance of hierarchical systems is crucial for their health management in many mission-critical industries. Due to the limited/absent reliability data and engineering knowledge available at the system level and the complex system structure, system-level reliability assurance is challenging. To meet with these challenges, the dissertation proposes a generic, flexible and recursive multi-level information aggregation framework by systematically utilizing multi-level reliability information throughout a system structure to improve the performance of a variety of system reliability assurance tasks. Specifically, the aggregation approach is first present to aggregate complex reliability data structure (e.g., failure time data with covariates and different censoring) with less distribution assumptions to improve accuracy of system-level reliability modeling. The system structure is mainly restricted to the hierarchical series-and-parallel system with independent intra-level components and/or sub-systems. Then, the aggregation approach is extended to accommodate multi-state hierarchical system by considering both probabilistic inter-level failure relationships and cascading intra-level failure dependency. Last, the aggregation approach is incorporated into the design of system-level reliability demonstration testing to achieve the potential sample size reduction. Different demonstration testing strategies with and without information aggregation are comprehensively compared with closed-form conditions obtained. A series of case studies have also been conducted to demonstrate that the proposed aggregation methodology can successfully improve the system reliability modeling accuracy and precision, and improve the cost-effectiveness of the system reliability demonstration tests.