A tool for system monitoring based on artificial neural networks

Di Bono M. G., Pieri G., Salvetti O.

Artificial Neural Networks  Self-Organising Maps  Prediction Systems  Life Cycle Monitoring 

A research has been carried out finalized to the definition of a methodology useful for the diagnosis and prediction of the correct evolution state of physical systems. In this paper we present a related model and a specific network topology for the considered problem. In particular, the prediction procedure is based on a 'Self Organizing Map'(SOM) and an 'Error Back-Propagation'(EBP) networks combined to form a hierarchical architecture. The network system has been developed and tested using data furnished by Alenia and consisting in sensorial data (FBG, Fiber Bragg Grating) and multi-format descriptive data regarding evaluation (SB). The obtained results have shown that the developed methodology is a promising tool for the diagnosis activity.

Source: WSEAS transactions on systems 3 (2004): 746–751.

Publisher: WSEAS Press, Athens