Efficient Model-Based Diagnosis of Sequential Circuits

Abstract

In Model-Based Diagnosis (MBD), we concern ourselves with the health and safety of physical and software systems. Although we often use different knowledge representations and algorithms, some tools like satisfiability (SAT) solvers and temporal logics, are used in both domains. In this paper we introduce Finite Trace Next Logic (FTNL) models of sequential circuits and propose an enhanced algorithm for computing minimal-cardinality diagnoses. Existing state-of-the-art satisfiability algorithms for minimal diagnosis use Sorting Networks (SNs) for constraining the cardinality of the diagnostic candidates. In our approach we exploit Multi-Operand Adders (MOAs). Based on extensive tests with ISCAS-89 circuits, we found that MOAs enable Conjunctive Normal Form (CNF) encodings that are significantly more compact. These encodings lead to 19.7 to 67.6 times fewer variables and 18.4 to 62 times fewer clauses. For converting an FTNL model to CNF, we could achieve a speed-up ranging from 6.2 to 22.2. Using SNs fosters 3.4 to 5.5 times faster on-line satisfiability checking though. This makes MOAs preferable for applications where RAM and off-line time are more limited than on-line CPU time.

AutoConf: New Algorithm for Reconfiguration of Cyber-Physical Production Systems

“…For diagnosis, hybrid systems are modeled using hybrid automata and qualitative fault models [11] or Bayesian networks, that model the dependencies between sensors and faults qualitatively [19]. For solving, Feldman et al [15] presented an enhanced SATbased algorithm to MBD, for that the system and observations are modeled in propositional logic. Borgo et al [9] used automated planning for the optimal control of manufacturing systems.…”

“…[13], [17], [18], MBD, e.g. [9], [11], [15], MAS, e.g. [7], [20], [21], I4.0 [24], [26], [31], and ontologies, e.g.…”

“…To minimize modeling efforts, our approach also uses a qualitative control model (R1.3). MBD and ontologies rely on logical reasoning enabling intuitive modeling and the use of established solvers [15], [29]. To build on these advantages, our approach uses propositional logic (R1.4).…”

“…Hence, given causal graphs and observations, reconfiguration works completely automatically. Propositional logic is used for several reasons: It (1) enables the direct integration of expert knowledge in form of process specifications and domain knowledge due to intuitive modeling [15]; (2) reduces the underlying search space to the relevant configurations, since the logical formula models constraints coming from the CPPS; and (3) allows for fast solving, sacrificing the optimal solution for a quick identification of an arbitrary solution. Since reconfiguration is a search problem, it could also be directly implemented in search.…”

“…information about causal dependencies in the system, and a binary validity of configurations [6], and (R3.3) enable a direct integration of expert knowledge and intuitive modeling. Propositional logic is used widely for diagnosis [12] and planning [9] since it mimics human reasoning [15]. If it is well suited for reconfiguration of CPPS has not been examined yet.…”