Using reliability analysis to support decision making in phased mission systems

Abstract

Because of the environments in which they will operate, future autonomous systems must be capable of reconfiguring quickly and safely following faults or environmental changes. Past research has shown how, by considering autonomous systems to perform phased missions, reliability analysis can support decision making by allowing comparison of the probability of success of different missions following reconfiguration. Binary decision diagrams (BDDs) offer fast, accurate reliability analysis that could contribute to real‐time decision making. However, phased mission analysis using existing BDD models is too slow to contribute to the instant decisions needed in time‐critical situations. This paper investigates 2 aspects of BDD models that affect analysis speed: variable ordering and quantification efficiency. Variable ordering affects BDD size, which directly affects analysis speed. Here, a new ordering scheme is proposed for use in the context of a decision‐making process. Variables are ordered before a mission, and reordering is unnecessary no matter how the mission configuration changes. Three BDD models are proposed to address the efficiency and accuracy of existing models. The advantages of the developed ordering scheme and BDD models are demonstrated in the context of their application within a reliability analysis methodology used to support decision making in an unmanned aerial vehicle.

Reliability assessment of man‐machine systems subject to probabilistic common cause errors

“…The set of reduced ordered BDDs for the model is unique and the order of variables along each path from the root node to the end node is the same. For a BDD, if the event occurs, then the BDD is traversed along the 1‐branch, otherwise, it is traversed along its 0‐branch 36,37 . The BDD is traversed in this way until a terminal node, and the model algorithm can be expressed as $$$$\begin{equation}f = ITE\left[ {X,{f}_1,{f}_0} \right] = X{f}_1 + \overline X {f}_0\end{equation}$$$$where $$ITE$$ represents the if—then—else form, $$X$$ represents that the event occurs, that is, $$X = 1$$.…”

“…For a BDD, if the event occurs, then the BDD is traversed along the 1-branch, otherwise, it is traversed along its 0-branch. 36,37 The BDD is traversed in this way until a terminal node, and the model algorithm can be expressed as…”

Efficient reliability analysis of dynamic k‐out‐of‐n phase‐AND mission systems

“…Analytical modeling methods are often computationally more efficient than simulations and can provide accurate analysis results. Examples include state space models (eg, Markov chains, 10,14,15 semi‐Markov processes, 16,17 and Petri Nets 18,19 ), combinatorial methods (eg, Boolean algebra‐based decision diagrams 20–23 and universal generation functions 24,25 ), modular approaches, 26,27 and recursive methods 28–31 . The system reliability model constructed in the analytical method can be reused for reliability analysis under different component parameter values.…”