A combined modeling and analysis method for probabilistic common cause failures in phased-mission system

Virtualization is one of the main features of cloud computing systems, which enables building multiple virtual machines on a single server. However, this feature brings new challenge in reliability modeling, as the failure of the server will make all its co-located virtual machines inoperable, which is a typical common-cause failure. To satisfy the demand of the cloud computing system, the reliability of the system is defined as the probability that at least a given number of virtual machines are operable. State-space enumeration is one method to calculate such reliability; however, due to the large number of combinations, it is time-consuming and impractical. To solve this problem, we propose a simplified reliability analysis method based on fault tree and state-space models. Two illustrative examples are studied to show the process and the effectiveness of our method. State enumeration and Monte Carlo simulation are also used to prove the correctness of our method as back-to-back verifications. Compared to the reliability analysis without considering common-cause failures, our results are quite different, which illustrates the necessity of considering common-cause failures in the reliability of cloud computing systems.

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Selective maintenance optimization for random phased-mission systems subject to random common cause failures

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x18791724 ◽

2018 ◽

Vol 233 (3) ◽

pp. 379-400 ◽

Cited By ~ 1

Author(s):

Xisheng Jia ◽

Wenbin Cao ◽

Qiwei Hu

Keyword(s):

Single Phase ◽

Failure Behavior ◽

Maintenance Optimization ◽

Simulation Approach ◽

Common Cause ◽

Proposed Model ◽

Common Cause Failures ◽

Selective Maintenance ◽

Mission System ◽

Maintenance Model

In both industrial and military fields, there is such a kind of complicated system termed as phased-mission system, which executes missions composed of several different phases in sequence. The structure, failure behavior, and working conditions of such a system may change from phase to phase. The duration of each phase of such a system involved is random and follows a probability distribution, and the system may suffer some events resulting in simultaneous failures of different elements with different probabilities. In order to guarantee such a system completes the phased-mission successfully, a selective maintenance model for random phased-mission systems subject to random common cause failures is proposed to optimally identify a subset of maintenance activities to be performed on some elements of the system. Thereinto, a novel analytic model is developed to estimate the probability of the maintained random phased-mission system successfully completing the phased-mission, and we compare it with a well-known Monte Carlo Simulation approach. Finally, the proposed selective maintenance model has been successfully applied to an artillery weapon system. Comparative analysis is carried out to compare the proposed model with the traditional ones, including selective maintenance models for deterministic phased-mission systems and deterministic single-phase mission systems. The results show that ignoring some mission properties (e.g. randomness and multiple phases) in selective maintenance optimization will lead to (1) incorrect system and mission modeling, (2) incorrect computation of the probability of the random phased-mission system successfully completing a mission, and/or (3) nonoptimal selective maintenance options.

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An implicit method for probabilistic common cause failures in phased-mission system

2017 Second International Conference on Reliability Systems Engineering (ICRSE) ◽

10.1109/icrse.2017.8030751 ◽

2017 ◽

Author(s):

Huan Wu ◽

Jian Jiao ◽

Runjiang Zhao ◽

Zhiwei Chen

Keyword(s):

Implicit Method ◽

Common Cause ◽

Common Cause Failures ◽

Mission System

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RELIABILITY MODELING AND ANALYSIS OF COMPLEX HIERARCHICAL SYSTEMS

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539305001963 ◽

2005 ◽

Vol 12 (06) ◽

pp. 477-492 ◽

Cited By ~ 12

Author(s):

LIUDONG XING

Keyword(s):

System Level ◽

Hierarchical Systems ◽

Reliability Modeling ◽

Common Cause ◽

Modeling And Analysis ◽

Dynamic Fault Tree ◽

Common Cause Failures ◽

Computer Based ◽

Imperfect Coverage ◽

Different Levels

In this paper we consider the problem of reliability modeling and analysis of hierarchical computer-based systems (HS) with modular imperfect coverage (MIPC) and common-cause failures (CCF). The MIPC and CCF can cause vertical dependence that runs through different levels of the system as well as horizontal dependence that runs across components or modules on the same system level. The consideration of these dependencies poses unique challenges to existing HS reliability analysis methods. We propose an efficient decomposition and aggregation approach named EDA-HS to the reliability evaluation of complex hierarchical systems with both MIPC and CCF as one way to meet the above challenges in an efficient and elegant manner. Our approach is to decouple the effects of both MIPC and CCF from the combinatorics of the solution. The approach is represented in a dynamic fault tree by a proposed probabilistic functional dependency gate and a proposed CCF gate modeled after the existing FDEP gate. We present the basics and advantages of the EDA-HS approach by working through an analysis of an example HS subject to MIPC and CCF.

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