Reliability analysis of network systems subject to probabilistic propagation failures and failure isolation effects

Abstract

In this article, the reliability of network systems subject to probabilistic propagation failure and failure isolation effects is considered. Probabilistic propagation failure is the failure of some components in a system, which will cause other components to fail with certain probabilities. Probabilistic propagation failure exists in various network systems, such as computing network system and nuclear power generating network system. Failure isolation means that the failure of a trigger component will lead to its corresponding dependent components being isolated from the network system. Since the failure isolation effect is activated only when the failure of trigger components occurs before the occurrence of probabilistic propagation failure, there exists a competing failure in the time domain between the failure of a trigger component and the components with probabilistic propagation failure effect. If a trigger component failure occurs first, the system is insensitive to any failures of components being isolated. In this article, a combinatorial method based on binary decision diagram is proposed to analyze the reliability of the network systems subject to probabilistic propagation failure and failure isolation effects. The method can be applied to any network system and any type of lifetime distribution of the system components. As an example, a wide area network system is analyzed. Some numerical results about reliability indexes are provided to verify the feasibility and accuracy of the proposed method.

Reliability Analysis of Correlated Competitive and Dependent Components Considering Random Isolation Times

“…The combinatorial algorithm is used to solve the reliability analysis problem of single-stage [23] and multi-stage [24] systems in deterministic competitive faults. For probabilistic competitive faults [25], random fault propagation time [26] is considered. The failure propagation time [27] is considered from the multi-function dependency group and the cascading [28] behavior is considered in competitive failure.…”

Reliability analysis of discrete-time multi-state star configuration power grid systems with performance sharing

“…The reliability evaluation and optimization of MSSs have been extensively studied by many scholars since they have proven to be more precisely than the classical reliability theory of the binary state systems in process of the lifedegradation. Based on the reliability theory of MSSs, a variety of multi-state reliability models with traditional system configurations are studied, including multi-state seriesparallel systems [5][6], multi-state k-out-of-n systems [7] and multi-state network systems [8]. Beyond that, some extended MSSs of the specific configurations can also be found in reliability engineering field, e.g., fuzzy multi-state systems [9], multi-state phased mission systems [10], multi-state power grid systems [11][12][13] and so on.…”

Evaluation for machine tool components importance based on improved LeaderRank

“…1 Component fault transfer probability is a direct representation of fault transitivity between system components. 2,3 This parameter directly affects the importance of each component, which in turn affects machine maintenance cycle. Therefore, studying the fault transfer probability of machine tool components and the importance of components is necessary.…”