Few-shot remaining useful life prognostics through auxiliary training with related dataset

Predicting the remaining useful life of equipment can help organizations improve efficiency, reduce costs and enhance safety by enabling them to plan maintenance and repairs, and optimize their use of equipment. A major challenge in this field is the development of accurate deep learning models, particularly when data is limited to a few run-to-failure trajectories. Traditional methods, such as pre-training on a larger dataset followed by fine-tuning on the main data, often fall short under these constraints. To address this challenge, we propose an auxiliary training approach that integrates auxiliary objectives from related but distinct datasets. This approach enriches the learning process, utilizing knowledge from a broader data range and acting as a regularization mechanism to improve generalization from limited data. The effectiveness of the proposed method is demonstrated by experiments on two well-known public datasets, CMAPSS and N-CMAPSS, across eight distinct settings, and is shown to outperform state-of-the-art approaches such as single-task learning and pre-training followed by fine-tuning.