Neural fault detection of an adaptive controlled beam

Model-based observers seem to be the most evolving techniques during the past years, but depending on the complexity of the monitored system, they may become impractical. Non-model-based methods for fault detection are suitable for these complex cases, and artificial neural networks are likely to provide the necessary features. A comparison between these two methods is conducted in this paper, focusing on the structural fault detection in a cantilevered beam. This system, despite being a simple structure, permits a good insight of the characteristics of the two methods. Two structural faults are presented: a simulated crack on a finite element model of the beam, and a mass variation on an experimental test-bed. Both the simulation and the experimental results infer that neural networks may be a good option for fault detection in complex systems.