An intelligent diagnosis method using fault feature regions for untrained compound faults of rolling bearings

Bearing faults of rotating machinery are common compound faults, and diverse fault categories are coupled, which makes it challenging to achieve state monitoring. For this purpose, a fault diagnosis method based on fault feature region (FFR) detection for bearings is proposed. The key point is an FFR proposal network for several regions with fault features from training samples. This process only uses the single fault data. These obtained regions are used as the training dataset of a module based on a deep belief network. In the application, if the output probabilities satisfy the discrimination terms, then an untrained compound fault state is output. Furthermore, the bearing compound fault dataset is employed to assess the diagnosis performance. The results reveal that the diagnosis accuracy of compound faults and the overall accuracy exceeds 80%. This fascinating discovery proves the superiority of the proposed approach to achieving compound fault diagnosis for rolling bearings.