Abstract
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.
Highlights
Ere are currently two main methods for assessing the mission reliability and success probability: analytical methods and simulation methods. e analysis method can be divided into combinatorial model method [3,4,5,6], and state space method [7,8,9,10]
Combinatorial model method includes reliability block diagram method and fault tree analysis method [6]. e fault tree method can be combined with binary decision diagram (BDD) [3,4,5] and its derivative methods such as multivalued decision diagram (MDD) [11], aggregated binary decision diagram (ABDD) [12], and logarithmically encoded BDD (LBDD) [13]. ese methods make reliability problems easier to model and calculate in multistate phased mission systems (MS-PMS)
State space methods include Markov’s method [8,9,10] and Petri net method [7], both of which are based on stochastic process theory. e Markov method combined with the universal generating function [14] can Mathematical Problems in Engineering effectively solve the state space explosion. e analytical method can effectively analyze the system reliability in a specific mission, but it cannot calculate the success probability involving a specific mission. e idea of the analytical method to deal with PMS is to connect different phases in series, and one unit is regarded as different units in different phases. erefore, the phase dependencies of components need to be considered. e simulation method has good generality and can effectively solve the system reliability and success assessment
Summary
Downtime remaining distance can be completed on time. erefore, the OS is based on 100% power output during the mission. The goal of Scenario III has been extended 1 arriving at the destination at the prescribed time; 2 being still in the available state (assuming that all states except downtime are available) at the end of the navigation mission; 3 moving at high speed at any time during the combat. E complete process of generating each discrete event mainly goes through three modules: the sampling method selection module, the system state transition module, and the mission success decision module. E mission success decision module determines the system performance output according to the current system state and updates the completed and uncompleted workload to advance the mission progress. (1) Figure 6 shows the detailed mission simulation process where Ok′ (g(j)) is the reachable set of transitions in the current system state, and the sampling method is determined according to Ok′ (g(j)). Mission result type Mission success Over speed Unfinished maintenance Fatal fault
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