A first-order meta learning method for remaining useful life prediction of rotating machinery under limited samples

Abstract

Accurately predicting the remaining useful life (RUL) of rotating machinery is a challenging task in the field of Prognostics and Health Management (PHM). In practical applications, the number of samples in the target domain is often insufficient. To address this issue, we propose a First-Order Meta-Learning Network (FOMLN) to tackle the problem of equipment RUL prediction under limited samples. First, a meta-learner is constructed based on Conformer, combining the advantages of the self-attention mechanism and convolutional neural networks, enhancing the model's ability to capture both local and global features. Then, a dual-loop meta-learning strategy is designed: the inner loop learns at the sample level, modeling and updating parameters for specific tasks, while the outer loop updates the meta-parameters through task-level learning, improving the model's generalization across different tasks and its adaptability to new tasks under limited sample conditions. Extensive experimental results on the C-MAPSS dataset validate the effectiveness of the proposed method. Moreover, a practical application case study is introduced, demonstrating the model’s ability to predict the RUL of slurry pumps in an industrial site under few-shot scenarios, highlighting its potential for real-world applications.