Evidential network-based failure analysis for systems suffering common cause failure and model parameter uncertainty

Abstract

The fault tree analysis has been extensively implemented in failure analysis of engineered systems. In most cases, the probabilities of basic events, e.g. components’ failures, are represented by crisp values in the fault tree analyses. However, due to lack of knowledge, scarcity of failure data, or vague judgments from experts, it may produce parameter uncertainty associated with degradation models of components/systems, and such model parameter uncertainty can be quantified by the epistemic uncertainty. In addition, the common cause failure, related to the simultaneous failures of two or more components caused by physical interactions or shared environments, often exists in advanced engineered systems and computing systems. In this paper, by considering both the common cause failure and the epistemic uncertainty associated with model parameters, an evidential network model embedded with common cause failure is proposed to facilitate system failure analysis. The detailed transformations from some logic gates of a fault tree to an evidential network model are given. Moreover, the conditional belief mass tables are constructed to quantify the dependency between the states of components and the entire system. An engineering case of an aero-engine oil system, together with comparative results, is presented to demonstrate the effectiveness of the proposed evidential network model.

Common cause and load-sharing failures-based reliability analysis for parallel systems

“…Alizadeh et al [1] introduced the impact of common cause failure on the system reliability using Markov analysis technique. Zuo et al [23] analyzed the system failure suffering common cause failure. Fan et al [2] developed a new model for common cause failures considering components degradation based on mathematical framework of Stochastic Hybrid Systems.…”

Optimal element allocation and sequencing of multi-state series systems with two levels of performance sharing

“…[14][15][16][17] (iv) The network system, whose reliability is defined as the probability that its output can satisfy a pre-specified demand of a stochastic network. [18][19][20] (v) The phased mission system, which intends to accomplish different missions in consecutive phases. [21][22][23] To evaluate reliability of the abovementioned multi-state systems, different approaches are proposed in the literature, which include the stochastic process approach, [24][25][26][27] the universal generating function technique, [28][29][30][31] the Monte Carlo simulation, [32][33][34] and binary decision diagrams.…”

Fault Diagnosis Strategy for Complex Systems Based on Multi-Source Heterogeneous Information Under Epistemic Uncertainty

“…However, this method is relatively complicated and requires a large amount of calculations. Reference [12] discussed a detailed transformation from a logic tree of a fault tree to a dynamic evidence network model and an aero-engine oil system was used to verify the effectiveness of the proposed evidence network model.…”