Reliability analysis of fail-safe systems with heterogeneous and dependent components subject to random shocks

Abstract

In many significant safety systems, the fault tolerance of a fail-safe system has been a fundamental design attributed to obtaining more higher reliability. However, in most practical scenarios, the components in a fail-safe system suffer from additional random shocks, and one component’s failure may increase the failure probability of other surviving components. This article analyzes the effects of survived probabilities and components lifetimes on the reliability of fail-safe systems with statistically dependent components subject to random shocks in terms of the usual stochastic and hazard rate orderings. Sufficient conditions are established by means of transformation functions, underlying component lifetime distributions, and majorization orders. Some numerical examples are also presented for illustrating our theoretical findings.

Optimal redundancy allocations for series systems under hierarchical dependence structures

“…This dependence may be implied by common shocks affecting system components such as high temperatures and electrical impulses of large magnitudes (cf., Gao et al 25 . and Zhang et al 26 …”

“…Generally, the components' lifetimes are dependent as they operate in a common environment and suffer from external stress simultaneously. This dependence may be implied by common shocks affecting system components such as high temperatures and electrical impulses of large magnitudes (cf., Gao et al 25 and Zhang et al 26 ). Additionally, the lifetimes of redundancies and the component are also usually dependent in each node of the system, resulting in a hierarchical dependence structure among all components and redundancies.…”

Systematic Reliability Optimization (ASRO)

“…Our systematic review examines the latest techniques, particularly AI-based methods, for optimizing design and management of complex cyber-physical systems where failures can have severe economic and safety impacts [11] [12]. While existing surveys examine model-based [13] or component focused [14] reliability techniques, a comprehensive investigation comparing emerging data-driven methods is lacking. We conduct an empirical review of over 50 studies since 2016 benchmarking optimization approaches across industry verticals.…”