Model-based fault diagnostics of nonlinear systems using the features of the phase space response

A novel algorithm is presented in this paper for the diagnostics of nonlinear systems based on the idea of estimating the parameters of the system using features of the nonlinear response. The method combines statistical features of the nonlinear response and capabilities of artificial neural networks in data fitting with the objective of estimating the parameters of a defective nonlinear system. New features extracted from the density distribution of position and velocity signals are introduced to characterize the complex topologies of the phase plane response in periodic and multi-periodic domains. A nonlinear pendulum is used for experimental validation of the procedure. The results show that, with appropriately selected features of the response, the parameters of the nonlinear system can be estimated with an acceptable accuracy.