Increasing complexity and magnitude of technical systems demand an accurate fault localization in order to reduce maintenance costs and system down times. Resting on solid theoretical foundations, model-based diagnosis provides techniques for root cause identification by reasoning on a description of the system to be diagnosed. Practical implementations in industries, however, are sparse due to the initial modeling effort and the computational complexity. In this paper, we utilize a mapping function automating the modeling process by converting fault information avail- able in practice into propositional Horn logic sentences to be used in abductive model-based diagnosis. Furthermore, the continuing performance improvements of SAT solvers motivated us to investigate a SAT-based approach to abductive diagnosis. While an empirical evaluation did not indicate a computational benefit over an ATMS-based algorithm, the potential to diagnose more expressive models than Horn theories encourages future research in this area.