Automated fault diagnosis using neuro-fuzzy systems

Abstract

This paper presents a novel approach to integrating quantitative and qualitative information in faultdiagnosis, based on the use of neuro-fuzzy systems. In this approach the residuals are generated and evaluated via a B-Spline functions network. The configuration adopted allows the designer to both extract and include symbolic knowledge from the trained network. The diagnosis approach is put to the test through a digital simulation study of a non-linear two-tank system.