Monte Carlo simulation of complex system mission reliability

Lewis, E.E.; Boehm, Franz; Kirsch, Christopher; Kelkhoff, B. P.

doi:10.1145/76738.76803

Cited by 8 publications

(8 citation statements)

References 3 publications

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“…It is difficult to sample the mission failure when simulating such a high-reliability system. In order to increase the number of samples of failure transition in the mission time, Lewis et al [15] proposed forced transition (FT). Assuming the mission time is T, in order to obtain a sample in [t ′ , T], the sampling space of the state transition must be modified.…”

Section: Accelerated Sampling Methods For Multistate System Simulationmentioning

confidence: 99%

“…e meanings of x and (1 − x) are the proportions of failure transitions and repair transitions to all state transitions in this sampling, respectively. x usually ranges from 0.5-0.7 [15]. en, the failure transition rate will be much higher than the one that sampled in the original space.…”

Section: Failure Biasingmentioning

confidence: 99%

“…And it is less affected by the complexity of the system structure and mission requirements, which will not cause greater difficulties in the modeling method, model processing, and calculation. e core method of simulation is the Monte Carlo method [15][16][17] which is used to generate random events to simulate the behavior of the system in specific mission.…”

Section: Introductionmentioning

confidence: 99%

“…So, there is no problem of phase dependency of components. However, due to the low calculation efficiency and large variance of the calculation results in the application process, specific sampling techniques, such as forced transition (FT) and failure biasing (FB), need to be adopted [15,16,18].…”

Section: Introductionmentioning

confidence: 99%

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An Accelerated Simulation Approach for Multistate System Mission Reliability and Success Probability under Complex Mission

Yang

Liu

2020

Mathematical Problems in Engineering

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The mission reliability and success probability estimation of multistate systems under complex mission conditions are studied. The reliability and success probability of multistate phased mission systems (MS-PMS) is difficult to use analytic modeling and solving. An estimation approach for mission reliability and success probability based on Monte Carlo simulation is established. By introducing accelerated sampling methods such as forced transition and failure biasing, the sampling efficiency of small-probability events is improved while ensuring unbiasedness. The ship’s propulsion and power systems are used as applications, and the effectiveness of the method is verified by a numerical example. Under complex missions, such as missions with different mission time and their combinations, and phased-missions, the proposed method is superior in small-probability event sampling than the crude simulation method. The calculation example also studies the influence of mission factors or system reliability and maintainability factors on system availability and mission success probability, and analyzes the relationship between different mission types and system availability and success probability.

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Section: Accelerated Sampling Methods For Multistate System Simulationmentioning

confidence: 99%

Section: Failure Biasingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Accelerated Simulation Approach for Multistate System Mission Reliability and Success Probability under Complex Mission

Yang

Liu

2020

Mathematical Problems in Engineering

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“…Therefore, it is inapplicable to aircraft mission reliability analysis because aircraft cannot be repaired during mission spans. Another approach defined as phased‐mission reliability model has been well studied for the reliability analysis of phased‐mission systems . The biggest challenge this method encounters lies in that it limits the possibility of load conditions to a very limited extent and cannot make flexible adjustments according to an actual mission situation.…”

Section: Introductionmentioning

confidence: 99%

Aircraft reliability assessment for an individual mission based on recordable flight parameters

Wang

Guan

2018

Quality & Reliability Eng

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Reliability of aircraft varies with different missions, because load conditions are always diverse. In limited sample size circumstances, convention aircraft mission reliability assessment method can only give an average result for a kind of mission. Motivated by the practical need of reliability assessment for an individual mission, this paper constructs a novel method, which can assess reliability for a specific mission based on flight parameters. The mission reliability model can be established according to the relationship between flight loads, flight parameters, and reliability via adopting fault data and flight parameters synthetically. Statistical inference of aircraft failure rate is proposed, and then, the mission reliability can be assessed according to the fault data of the focused aircraft. It is worth noticing that the proposed method can also assess the reliability for a mission category via conducting an average calculation across all individual missions belonging to the target mission category. An illustrative example shows that the proposed method not only can give reliability assessments with a same precision with existing approaches for a specific mission category but also can reasonably assess the reliability regarding an individual mission.

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Modelling and simulation of repairable mechanical systems reliability and availability

Kumar

Jain

Soni

2019

Int J Syst Assur Eng Manag

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Markov approach is applicable for reliability and availability modelling when time to failure and repair follow an exponential distribution. Since failure time of mechanical components follows Weibull distribution, Markov approach cannot be employed to these systems. In present work, Semi-Markov model, which is appropriate for repairable mechanical systems, is considered. In structural dependency, once a unit of a repairable system is failed due to one or more of its constituent components, the entire unit is taken for repair. Therefore, the repairs are considered at the unit level. This feature of structural dependency in the proposed approach addresses the problem of larger state space. The states at the unit level are derived from the component states to develop the system model. The solutions for reliability and availability are obtained using the Monte Carlo simulation. The suggested approach is realistic than the existing software's such as Rapid Algorithmic Prototyping Tool for Ordered Reasoning, Blocksim, etc., as failures and repairs are considered at different hierarchical levels. The recommended approach is illustrated for a centrifugal pumping system.

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Monte Carlo simulation of complex system mission reliability

Cited by 8 publications

References 3 publications

An Accelerated Simulation Approach for Multistate System Mission Reliability and Success Probability under Complex Mission

An Accelerated Simulation Approach for Multistate System Mission Reliability and Success Probability under Complex Mission

Aircraft reliability assessment for an individual mission based on recordable flight parameters

Modelling and simulation of repairable mechanical systems reliability and availability

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