Experimental application of nonlinear minimum variance estimation for fault detection systems

A novel model-based algorithm for fault detection (FD) in stochastic non-linear systems is proposed. The Nonlinear Minimum Variance (NMV) estimation technique is used to generate a residual signal which is then used to detect actuator and sensor faults in the system. The main advantage of the approach is the simplicity of the nonlinear estimator theory and the straightforward structure of the resulting solution. Simulation examples are presented to illustrate the design procedure and the type of results obtained. The results demonstrate that both actuator and sensor faults can be detected successfully.