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This paper presents the Probabilistic General Diagnostic Engine (PGDE), a novel method of offline consistency-based fault isolation. Many existing proposals require qualitative logic mod- els for consistency-based diagnosis due to their ability to speed the search for conflict sets through the use of an ATMS. However, for many applications, quantitative dynamic models are preferred or al- ready available. The key strength of the PGDE is that it allows the use of any modelling language for which an appropriate calculation en- gine can be written. It also offers graceful degradation in the presence of uncertainty, commonly caused by noise or modelling errors. Fi- nally, given perfect knowledge, it can be shown that the PGDE com- putes the same result as existing consistency-based diagnosis meth- ods. To demonstrate the performance of the algorithm, we have used a quantitative dynamic model of the fluid power circuit of a single- degree of freedom hydraulic test bench and developed an appropri- ate calculation engine for computing consistency between measured values and predicted results. Various failures were generated on the physical test bench and the PGDE isolated the faults with approxi- mately 85% accuracy.