By Dan Lawesson.
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Extra info for An approach to diagnosability analysis for interacting finite state systems
Segment! crit:fail wait fail? down down! 2: The state transition relation of the ipol component. e Given a transition s → s we say that s is the source state and s is the target state. If s = s we also say that s is a successor state of s. A e transition s → s is called a loop transition iff s = s . A potentially reachable state of a component c is a state s n such e1 e2 en that init(c) → sn , where n ≥ 0. A compos1 → s2 . . sn−1 → nent c = (Σ, S , → , s0 , id) is called the reachable sub-component of c = (Σ, S, →, s0 , id) iff S are the potentially reachable states of c and → contains all (s, e, s ) ∈ → such that s ∈ S .
Thus, the term “components” will refer to memory components unless explicitly stated otherwise. Mathematical Foundation 27 We assume that components have disjoint sets of states. This does not limit the expressivity of our framework since a model with two distinct components with non-disjoint sets of states can be simulated by a behaviorally equivalent model where state names are renamed to ensure disjoint sets of states. To avoid unnecessary indexing of state names, we take the liberty of using non-disjoint sets of state names in some examples in this thesis.
Some use pattern recognition [NP05, HTP04] to find the fault given a pattern of observations, others use abduction based reasoning [TCPD95, AR86] or knowledge bases [SEM89, HCJK04]. Causal models [Pro02] make the fault isolation process more straightforward since the model in itself links observations to the causing faults. Stochastic models [TT05] introduce the ability to reason about likelihood of a certain fault given an observation. In this thesis we will focus on a model-based and deductive approach.
An approach to diagnosability analysis for interacting finite state systems by Dan Lawesson.