ハザード制約と時系列共変量を考慮した生存関数の深層学習と半教師あり余寿命予測

抄録

Accurate prediction of remaining useful life (RUL) is crucial for efficient maintenance of industrial equipment. Although various deep learning-based RUL prediction methods have been studied in recent years, effectively utilizing unlabeled data and long RUL data remains challenging. In this study, we apply a previously proposed method to the semi-supervised RUL prediction using time-series data. In this approach, a survival function modeled by neural networks is learned under hazard constraints. Introducing the survival function allows for consistent probabilistic handling of both labeled and unlabeled data. Experimental results on the CMAPSS dataset demonstrate that the proposed method outperforms baseline approaches.