Unknown Fault Diagnosis of Motors Based on Incremental Learning and Edge Computing

Abstract

Incremental learning (IL) provides a dynamic framework for expanding the classification capacity of data-driven systems, thereby facilitating unknown fault diagnosis (UFD) in motor systems. However, the necessity for a manually established training set with unknown fault samples for the retraining of IL models, combined with insufficient consideration of real time, presents significant challenges. To address these challenges, this article proposes an automatic IL method based on edge computing (AILEC) for UFD of motors. First, the method introduces a convolutional encoder based on a training guide-separation module (CETGM) and a feature similarity match (FSM) technique. These components are designed to function effectively at the edge after initial training. An IL method, based on edge joint training (EJT), is then proposed to extend the classifiable quantity of the model at edge end, based on UFD results derived from CETGM and FSM. The superiority of the proposed method is validated through experiments on a motor test rig. The results demonstrate that the approach achieves 99.99% accuracy for UFD, with an average accuracy of 98.84% across 4 to 10 incremental states. Additionally, the system delivers a model size of 0.5 MB, an inference time of 2.09 ms, and a model update time of 164 s. The proposed method outperforms several existing approaches in terms of accuracy and real-time processing capabilities. It provides an intelligent solution for UFD of motors, featuring continuous model updates and real-time IL.