Adaptive neural-networks-based fault detection and diagnosis using unmeasured states

Fault detection and diagnosis play important roles in modern engineering systems. A number of fault diagnosis (FD) approaches for nonlinear systems have been proposed. But, most of the achievements are based on the assumptions that the systems models are known, and states of systems are measurable. A novel FD architecture for a class of unknown nonlinear systems with unmeasured states has been investigated. A general radial basis function (RBF) neural network is used to approximate the model of unknown system, an adaptive RBF neural network with on-line updated centre, and the width vector of dasiaGaussiandasia function is used to approximate the model of fault. A nonlinear state observer is designed to estimate system states that are inputs to the neural networks. The stability analysis for the system is given, and the adaptive parameter-updating laws are derived using Lyapunov theory. Simulation examples are used to illustrate the effectiveness of the proposed method.