Adaptive Class-Incremental Learning Intelligent Fault Diagnosis Method for Unmanned Aerial Vehicle Rolling Bearings

As a key component of unmanned aerial vehicles (UAVs), the stable operation of motor bearings is of vital importance to the stability of UAVs. In view of the incomplete data set in the actual diagnosis process, samples not encountered during model training are highly likely to appear. This paper proposes an Adaptive Class-Incremental Learning(ACIL) intelligent fault diagnosis method. This method construct a ResNet framework embedded with Coordinate Attention as the base architecture for class-incremental learning. Furthermore, the Information Preservation Example Selection(IPES) method is utilized to alleviate catastrophic forgetting and update the model from the previous phase using knowledge distillation under coordinate attention. The effectiveness of this method is verified through experiments on the bearing test dataset. The results show that, both average incremental accuracy and average incremental forgetting rate achieve state-of-the-art performance, which means that the performance of the proposed method outperforms than those of other methods.