Fault diagnosis of a hydraulic actuator circuit using neural networks—an output vector space classification approach

Abstract

This paper presents a neural network approach to fault diagnosis of dynamic engineering systems based on the classification of surfaces in system output vector space. A simple second-order system is used to illustrate graphically the nature of the diagnosis problem and to develop theory. The approach is then applied to the diagnosis of a laboratory-based hydraulic actuator circuit. Results are presented for networks trained on both simulation and experimental data. An important achievement is the diagnosis of experimental faults using a network trained only on simulation data.