Hybrid intelligent diagnosis technology based on granular computing

Aiming at lacking hybrid modes and common algorithms in existing hybrid intelligent diagnosis, a new model of hybrid intelligent fault diagnosis based on granular computing is proposed. In the model, the core features sets (CFS) are extracted in different granular levels by the reduction algorithm based on neighborhood rough set, then, CFS are chosen to train artificial neural network and support vector machines as sub-classifiers in corresponding levels. And the results of sub-classifiers in different granular levels are combined by criterion matrix algorithm as output of hybrid intelligent diagnosis. The model is applied to fault diagnosis of roller bearings in high-speed locomotive. The application results demonstrate that the classification accuracy is raised with the increasing granular levels, and the accuracy of hybrid results is higher than the one of any sub-classifier. The proposed model exhibits the effect of granulation and the advantages complementation among different intelligent methods to provide a new way for hybrid intelligent diagnosis.