Fault diagnosis using rough sets and BP networks

This research presents a rough set and back propagation neural network based scheme for rolling bearings fault diagnosis. The scheme is designed to classify the fault type. Experiments results indicate that rough set is helpful to reduce dimensionality, discard deceptive features and extract an optimal subset from the raw feature set, and the proposed rough sets combined with BP neural network (RNN) classifier can identify roller bearing fault patterns effectively.