Traditional experiential knowledge-based diagnostic systems have suffered in their application to real-world problems due to their inability to handle unanticipated situations. Subsequently, diagnostic reasoning approaches have been developed that rely on a model of the device's normal operation to mitigate these shortcomings; these systems are called model-based diagnostic systems. The models of model-based systems are primarily a representation of the actual components of the device, which each have a defined behavior, along with a description of the topology of the device. The behavior of the device is simulated by introducing values into the device's inputs and propagating their effects throughout the interconnected components. Model-based systems often require an excessive amount of computation and data to arrive at a solution, primarily because information about any component in the model can only be utilized to make additional inferences about the components which are physical proximal to it in the device. The main focus of this article is a model of device operation that can be utilized to evaluate efficiently the ability of each component to account for a deviation in the device's state variables. The model presented here encodes the various constituents of the device at multiple levels of detail and abstraction, which are centered around the function or purpose of each concept.