In this paper we bring closer computation of consistency-based cardinality-minimal diagnosis and solving Max-SAT. We propose two algorithms for translating between those: (1) DIORAMA (DIagnOsis-based algoRithm for mAx-sat optiMizAtion) for translating cardinality-minimal consistency based diagnosis to Max-SAT and (2) MERIDIAN (Max-sat-basEd algoRIthm for DIAgNosis) for the other way around. While the former approach has been studied, solving Max-SAT instances with a diagnostic solver is, to the best of our knowledge, novel.

A variety of rule-based, model-based and datadriven techniques have been proposed for detection and isolation of faults in physical systems. However, there have been few efforts to comparatively analyze the performance of these approaches on the same system under identical conditions. One reason for this was the lack of a standard framework to perform this comparison.

Existing research in Model-Based Diagnosis (MBD) primarily concerns computation of a single (possibly multiple-fault) diagnostic candidate. This is unrealistic, as often multiple candidates cannot be discerned given a system description and an observation vector. It is also computationally more difficult to compute multiple minimal-cardinality diagnoses, as opposed to a single diagnosis. In this paper we analyze the theoretical and practical aspects of computing multiple minimal-cardinality diagnoses.