last column of Table \[table:embedding\_HyDE\], we report the running time it took for HyDE to solve the same problem instances. HyDE is a general purpose model-based diagnosis engine. HyDE uses models of nominal and faulty behavior of the system being diagnosed to make predictions about the expected trajectory of the system. This can be compared against the actual system trajectory as observed through sensors placed in the system to detect any discrepancies. HyDE then uses the detected discrepancies to perform a heuristic search over the fault space to select most likely candidates. One or more candidates consistent with the sensor observations are reported as the likely faults.

HyDE supports a wide variety of models and sensor types. The models can at different levels of abstractions, qualitative or quantitative in nature, be static or dynamic (differential equations), and include uncertainties. Sensors can be boolean, enumerations, real-valued or interval-valued. While this allows HyDE to be used in a wide variety of domains and problems, HyDE cannot take advantage of special characteristics of systems and domains unless explicitly modeled.

Figure \[fig:diagnosis\]a shows the HyDE model of electrical network described in the previous section. HyDE uses the model to predict the expected readings of 