Reserving embedding space for new fault types: A new continual learning method for bearing fault diagnosis

Abstract

In complex operating environments, rotating equipment may continually generate new fault categories, affecting the safety of equipment operation, and the number of collected fault samples is limited, which make it difficult to establish a reliable diagnostic model. Few-shot continual learning (FSCL) can continuously learn and summarize fault knowledge from limited samples. These traditional FSCL models, when learning from limited samples of new types of bearing faults, exhibit model overfitting and catastrophic forgetting issues after self-updating (new model). This phenomenon significantly limits the reliability of traditional models. This paper presents a new approach coined as reserving embedding space for new fault types (RESNFT) for continual learning of newly occurred bearing faults for machinery fault diagnosis, which includes several key and new ideas. Preliminary, a class prototype (prototype vector) is derived to replace a classifier weight. Then, embedding space reserved by forcibly assigning real fault samples to both real and virtual categories can mitigate catastrophic forgetting. Finally, virtual faults formed by sample mixing guide new fault samples to the reserved embedding space, and realize continuous classification of new faults. Results show that the RESNFT can effectively alleviate the catastrophic forgetting and overfitting problems, and it is superior to other existing methods.