Robust multi-model fault detection and isolation with a state-space neural network

This paper presents an design of a Robust Fault Detection and Isolation (FDI) diagnostic system by the means of state-space neural network. First, an solution utilizing multimodel technique is described, in which a Single-Input MultiOutput (SIMO) system is decomposed into a number of Multi-Input Single-Output (MISO) and Single-Input Single-Output (SISO) models. Application of such models makes possible to calculate a set of residual signals required in evaluation process with a Model Error Modelling (MEM) to obtain diagnostic signals. In turn, to isolate faults the diagnostic signals together with defined binary diagnostic table are applied. For experimental verification of the proposed approach, the laboratory stand of Modular Servo is chosen. All necessary data were gathered with the Matlab/Simulink software.