Fault Diagnosis for Power Converters Based on Incremental Learning
Shiqi Zhang, Rongjie Wang, Libao Wang, Yupeng Si, Anhui Lin, Yichun Wang
Abstract
In practical fault diagnosis, the monitoring fault data is accumulated incrementally, it is necessary to detect the newly added fault data. To this end, this paper proposed a broad residual network (BRES) fault diagnosis method with incremental learning capability. Firstly, the deep feature representation of the raw data is obtained by the residual network, and the obtained features and corresponding labels are then updated to the BLS. For the newly collected data, the incremental learning of new fault modes is achieved by automatic feature extraction of the ResNet and the node expansion of the BLS. The effectiveness of the proposed method is verified by motor-driven converters fault diagnosis. Experimental results indicate that the method can effectively update the diagnosis model to incrementally learn new fault categories and new fault modes.