Application of Neural Networks for Detection of Changes in Nonlinear Systems

Abstract

A nonparametric structural damage detection methodology based on nonlinear system identification approaches is presented for the health monitoring of structure-unknown systems. In its general form, the method requires no information about the topology or the nature of the physical system being monitored. The approach relies on the use of vibration measurements from a “healthy” system to train a neural network for identification purposes. Subsequently, the trained network is fed comparable vibration measurements from the same structure under different episodes of response in order to monitor the health of the structure and thereby provide a relatively sensitive indicator of changes (damage) in the underlying structure. For systems with certain topologies, the method can also furnish information about the region within which structural changes have occurred. The approach is applied to an intricate mechanical system that incorporates significant nonlinear behavior typically encountered in the applied mechani...