Research on bearing fault diagnosis using a LAOP-LSTM meta-learning model

Begin This study investigates the use of the LAOP-LSTM meta-learning model for bearing fault diagnosis. Bearing fault diagnosis is crucial for improving system performance and extending its lifespan. Traditional diagnostic methods rely on expert experience and manual analysis, which are costly and inefficient. While deep learning has enhanced diagnostic automation and accuracy, issues like managing noise and reliance on data still remain. To address these issues, this paper proposes a novel approach based on LAOP-LSTM. By leveraging few-shot learning and meta-learning techniques, the model effectively adapts and learns quickly in scenarios with limited data. When applied to the CWRU bearing fault dataset, the proposed model demonstrates superior diagnostic accuracy and generalization capability compared to both traditional methods and other deep learning models.