Bearing fault diagnosis under various conditions using an incremental learning-based multi-task shared classifier

Rolling bearings are susceptible to failure because of their complex and severe working environments. Deep learning-driven intelligent fault diagnosis methods have been widely introduced and exhibit satisfactory performance. However, bearing fault diagnosis during various working conditions is challenging; catastrophic forgetting occurs when test data are gathered under different conditions. In this paper, we develop an incremental learning-based multi-task shared classifier (IL-MTSC) for bearing fault diagnosis under various conditions. We use a one-dimensional convolutional neural network model as the principal framework. Then, we create a knowledge distillation method that allows the model to retain learned knowledge. Finally, we use a shared classifier that operates under various scenarios to establish a unified structure. The method can continuously learn and preserve knowledge when diagnoses bearing faults under various working conditions. The results show that IL-MTSC optimally diagnoses bearing marks under various conditions.