Diagnosising faults by supervised and unsupervised learning

Neural networks provide a solution to overcome drawbacks of the quantitative fault diagnosis because first, they are capable to model off-line the behaviour of linear and non-linear systems. Secondly, they can also learn on-line the behaviour of a system requiring no priori knowledge about the system. The neural networks are particularly good for fault diagnosis of systems that have imperfect models and/or incomplete data. There are two basic learning methods of neural networks that are applied to fault diagnosis: supervised and unsupervised learning methods. To solve problem of priori unknown faults, unsupervised learning is used. The Counterpropagation network was selected to diagnose faults as result of analysis of supervised and unsupervised learning methods applied to neural networks.