1998 ◽  
Vol 12 (2) ◽  
pp. 239-260 ◽  
Author(s):  
Helge Langseth ◽  
Bo Henry Lindqvist

We consider a monotone multistate system with conditionally independent components given the component reliabilities, and random component reliabilities. Upper and lower bounds are derived for the moments of the random reliability function, extending results for binary systems. The second moment is given special attention, as this quantity is used to calculate the standard deviation of the system reliability estimate. The motivation for the paper is to establish a basis for uncertainty analysis and Bayesian estimation in connection with multistate systems.


Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Jinlei Qin ◽  
Zheng Li

With the increasing complexity of industrial products and systems, some intermediate states, other than the traditional two states, are often encountered during reliability assessments. A system with more than two states is called a multistate system (MSS) which has already become a general phenomenon in the components and/or systems. Moreover, common cause failure (CCF) often plays a very important role in the assessment of system reliability. A method is proposed to assess the reliability and sensitivity of an MSS with CCF. Some components are not only in a failure state that can cause failure itself, but also in a state that can cause the failure of other components with a certain probability. The components that are affected by one type of CCF make up some sets which can overlap on some components. Using the technology of a universal generating function (UGF), the CCF of a component can be incorporated in the expression of its UGF. Consequently, indices of reliability can be calculated based on the UGF expression of an MSS. Sensitivity analysis can help engineers to judge which type of CCF should be eliminated first under various resource limitations. Examples illustrate and validate this method.


2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Haojie Yang ◽  
Yifan Xu ◽  
Jianwei Lv

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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