ABSTRACT Remaining useful life (RUL) prediction is a key task in predictive maintenance, providing crucial insights into the health status and reliability of equipment. This paper proposes a novel RUL prediction method, GASF‐STC, which integrates Gramian angular summation field (GASF), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and contrastive learning techniques. The sensor data is transformed into both temporal features and two‐dimensional images to capture global temporal characteristics and local spatial correlations, followed by feature processing using CNN and RNN. Finally, contrastive learning is employed for feature fusion, ensuring a balanced contribution from both features. The proposed method is evaluated on the C‐MAPSS turbofan engine benchmark, and its generalization capability is further validated on the MIT Battery Aging Dataset. Experimental results demonstrate that GASF‐STC not only outperforms traditional models on the engine dataset but also maintains robust prediction performance under a completely different degradation mechanism. The results highlight the effectiveness of spatiotemporal feature fusion, significantly improving prediction accuracy and stability, making GASF‐STC a promising RUL prediction method for predictive maintenance applications.
Zhang et al. (Wed,) studied this question.