A general approach to non-linear output observer design using neural network models

Abstract

Predictive maintenance has become a familiar concept in industrial fault detection regime. The ability to detect early warning signals in systems in the form of small changes in dynamic behavior is essential to anticipate failures. In general accurate system models are an essential part in residual based fault detection. However, in complex nonlinear systems, the development of accurate models can be very difficult, thus usually other approaches are often selected. As an alternative to the nonlinear analytical models, neural networks have shown significant potential in accurately representing nonlinear systems. In this paper we show how a system identified by a neural network, and a nonlinear observer can be used to detect changes in system dynamics. Different methods for observer design are discussed. The experimental section show the observer implemented on a thermo fluid system. Several faults are introduced, and the observer prediction is compared to actual data.