Component ontological representation of function for diagnosis

Using function instead of fault probabilities for candidate discrimination during model based diagnosis has the advantages that function is more readily available, and facilitates explanation generation. However, current representations of function have been context dependent and state based, making them inefficient and time consuming. We propose classes as a scheme of representation of function for diagnosis based on component ontology principles, i.e., we define component functions (called classes) with respect to their ports. The scheme is space and time-wise linear in complexity, and hence, efficient. It is also domain-independent and scalable to representation of complex devices. We demonstrate the utility of the representation for the diagnosis of a printer buffer board