A GMDH NEURAL NETWORK BASED APPROACH TO PASSIVE ROBUST FAULT DETECTION USING A CONSTRAINTS SATISFACTION BACKWARD TEST

Abstract

This paper focus on the problem of passive robust fault detection using nonlinear models that include parameter uncertainty. The non-linear model considered here is described by a Group Method of Data Handling Neural Network (GMDHNN). The problem of passive robust fault detection using models including parameter uncertainty has been mainly addressed checking if the measured behaviour is inside the region of possible behaviours following what will be called in the following a forward test. In this paper, a backward test based on checking if there exists a parameter in the uncertain parameter set that is consistent with the measured behaviour is introduced. This test is implemented using interval constraint satisfaction algorithms which can perform efficiently in deciding if the measured state is consistent with the GMDHNN model and its associated uncertainty. Finally, this approach is tested on the servoactuator proposed as a FDI benchmark in the European Project DAMADICS.