Data Augmentation‐Driven Remaining Useful Life Prediction of Operational Amplifiers With Improved Conformal Uncertainty Quantification

ABSTRACT Accurate remaining useful life prediction is critical to the reliable operation of operational amplifiers in modern electronic systems. However, existing methods face two key challenges: predictions typically lack statistical guarantees on uncertainty quantification (UQ), and randomly missing monitoring data caused by sensor or transmission failures further degrade model performance. To address these issues, this paper proposes a data augmentation‐driven RUL prediction and conformal UQ method, termed DiffUQ. First, an Electronic Component Degradation Pattern Encoder (ECDPE) is constructed by integrating contrastive learning into a variational autoencoder framework to extract discriminative degradation representations from multivariate monitoring data. Building on these representations, a diffusion‐based data generation framework (DiffGen) equipped with a specially designed ST‐Denoiser is developed to impute and augment data with missing values while capturing the underlying spatio‐temporal correlations. Finally, deep metric learning is combined with enhanced conformal prediction to construct prediction intervals that retain a coverage guarantee under departures from exchangeability, with the loss of coverage bounded explicitly. Experiments on the accelerated degradation dataset of SX8042MF operational amplifiers demonstrate that DiffUQ achieves superior point prediction accuracy and uncertainty quantification compared to several state‐of‐the‐art baselines under various missing‐data ratios. Moreover, DiffGen consistently improves multiple underlying prediction models and outperforms widely used data augmentation methods such as CGAN, WGAN‐GP, VAE, and TimeVAE.