A general design technique for fault diagnostic systems
We put forward a design method for fault diagnostic systems (FDSs) by proposing a fault model and using the incremental hybrid learning algorithm which tightly combines symbolic learning and neural networks. It is capable of overcoming several shortcomings in existing diagnostic systems, such as the lack of universality, the unbalance in the use of fault prior knowledge and the dynamic data and the dilemma of stability and plasticity. Experiment showed the FDS implemented by this kind of method had a good diagnostic ability.