Fault diagnosis using quantitative and qualitative knowledge integration

This paper presents a novel approach to integrating quantitative and qualitative information in fault-diagnosis, and which is based on the use of associative B-spline functions. The underlying concept is to structure an artificial neural network which can model highly nonlinear systems efficiently, in a fuzzy logic format. The network could therefore be trained more rapidly and will also provide a linguistic description about the causes of faults. The diagnosis approach is put to the test through a digital simulation study of a nonlinear two-tank system.