Reliability analysis of large phased-mission systems with repairable components based on success-state sampling

Lu, Jiming; Xiao-yue, WU; Liu, Yiliu; Lundteigen, Mary Ann

doi:10.1016/j.ress.2015.05.010

Cited by 49 publications

(16 citation statements)

References 37 publications

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“…Xing et al [13,14] and Levitin et al [15] proposed BDD based methods for the reliability evaluation of PMSs with commoncause failures and propagated failures. Wang et al [16] and Lu et al [17] studied modular methods for reliability analysis of PMSs with repairable components, by combining BDDs with state-enumeration methods. While the BDD method has been shown to be a very efficient combinatorial method, it is still difficult to analyze large systems without considerable computational expense [1,12].…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis of general phased mission systems with a new survival signature

Huang

Aslett

Coolen

2019

Reliability Engineering & System Safety

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It is often difficult for a phased mission system (PMS) to be highly reliable, because this entails achieving high reliability in every phase of operation. Consequently, reliability analysis of such systems is of critical importance. However, efficient and interpretable analysis of PMSs enabling general component lifetime distributions, arbitrary structures, and the possibility that components skip phases has been an open problem.In this paper, we show that the survival signature can be used for reliability analysis of PMSs with similar types of component in each phase, providing an alternative to the existing limited approaches in the literature. We then develop new methodology addressing the full range of challenges above. The new method retains the attractive survival signature property of separating the system structure from the component lifetime distributions, simplifying computation, insight into, and inference for system reliability.

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Section: Introductionmentioning

confidence: 99%

Reliability analysis of general phased mission systems with a new survival signature

Huang

Aslett

Coolen

2019

Reliability Engineering & System Safety

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“…A number of recent publications (Levitin, Xing, Amari, & Dai, ; Lu, Wu, Liu, & Lundteigen, ; Ma & Trivedi, ; Peng, Zhai, Xing, & Yang, ; Wang, Xing, & Levitin, ; Wang, Xing, Peng, & Pan, to name a few) are devoted to analysis of the phased‐mission systems that somewhat resemble our setting. However, all referenced papers consider neither abort policies and the MSP‐survivability tradeoff, nor the influence of random shocks.…”

Section: Introductionmentioning

confidence: 99%

Optimal Abort Rules for Multiattempt Missions

2019

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Many real-world systems use mission aborts to enhance their survivability. Specifically, a mission can be aborted when a certain malfunction condition is met and a risk of a system loss in the case of a mission continuation becomes too high. Usually, the rescue or recovery procedure is initiated upon the mission abort. Previous works have discussed a setting when only one attempt to complete a mission is allowed and this attempt can be aborted. However, missions with a possibility of multiple attempts can occur in different real-world settings when accomplishing a mission is really important and the cost-related and the time-wise restrictions for this are not very severe. The probabilistic model for the multiattempt case is suggested and the tradeoff between the overall mission success probability (MSP) and a system loss probability is discussed. The corresponding optimization problems are formulated. For the considered illustrative example, a detailed sensitivity analysis is performed that shows specifically that even when the system's survival is not so important, mission aborting can be used to maximize the multiattempt MSP.

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“…In such a way, the failure and repair behaviors of each component are characterized. In [17], the authors use the same approach to analyze the reliability of an aircraft when separately considering climbing, cruising, and landing phases.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

k-out-of-n systems: An exact formula for the stationary availability and multi-objective configuration design based on mathematical programming and TOPSIS

Carpitella

Certa

Izquierdo

et al. 2018

Journal of Computational and Applied Mathematics

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Reliability and availability analyses are recognized as essential for guiding decision makers in the implementation of actions addressed to improve the technical and economical performance of complex systems. For industrial systems with reparable components, the most interesting parameter used to drive maintenance is the stationary availability. In this regard, the present paper proposes an exact formula for computing the system stationary availability of a k-out-of-n system. Such a formula is proved to be in agreement with the fundamental theorem of Markov chains. Then, a multiobjective mathematical model is formulated for choosing the optimal system configuration design. The Pareto front is developed using the Lexicographic Goal Programming (LGP) method, and the TOPSIS method is successively implemented to choose the k-out-of-n configuration that represents the best compromise between the considered objective functions. A numerical example is provided.

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Reliability analysis of large phased-mission systems with repairable components based on success-state sampling

Cited by 49 publications

References 37 publications

Reliability analysis of general phased mission systems with a new survival signature

Reliability analysis of general phased mission systems with a new survival signature

Optimal Abort Rules for Multiattempt Missions

k-out-of-n systems: An exact formula for the stationary availability and multi-objective configuration design based on mathematical programming and TOPSIS

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