Curriculum Learning Framework for Fault Diagnosis in Electric Motor Systems Based on Recurrent Neural Networks

The generalisation of Deep Learning (DL) models under extrapolated usage conditions, particularly in the context of fault diagnosis, remains a significant challenge. Traditional methods encounter difficulties in terms of adaptability, necessitating extensive retraining when confronted with evolving conditions and unanticipated fault patterns. Moreover, existing DL approaches often demonstrate a lack of robustness in dynamic environments due to their limited capacity for generalisation. To address these limitations, this work introduces a novel Curriculum Learning (CL)-based approach, the Residual Singular Value Decomposition Curriculum Learning algorithm (ReSVD-CL), which integrates a Long Short-Term Memory (LSTM) model to capture temporal dependencies in fault patterns and Singular Value Decomposition (SVD) preprocessing to enhance feature representation. A complexity-based criterion segments the dataset into subsets, which are gradually incorporated into training via a pacing function. A comparison of ReSVD-CL with existing methods, such as Baby-Step Curriculum Learning (BS-CL), One-Pass Curriculum Learning (OP-CL) and baseline approaches of fault diagnosis, demonstrates that ReSVD-CL attains superior classification performance, particularly in extrapolated scenarios. The approach facilitates continuous adaptation to new usage profiles without the necessity of full retraining, thereby reducing computational costs and accelerating convergence. The study maintains a strong diagnostic performance with minimal usage profiles, thus demonstrating the potential of combining CL with dimension reduction and temporal modelling to develop more efficient and adaptable fault diagnosis systems.