A Macro-Anchored Physics-Informed GAN for RUL Prediction Under Continuous Block Missing Data

Continuous block missingness in condition monitoring data poses a major challenge to the reliability of Remaining Useful Life (RUL) prediction for industrial equipment. Existing frameworks, including direct prediction from incomplete observations and decoupled two-stage imputation–prediction pipelines, suffer from a noticeable performance decline under prolonged data voids. Although Generative Adversarial Network (GAN)-based generative models have shown promise in data repair, they remain limited by dimensionality contamination from fixed-dimension architectures, purely data-driven over-smoothing that does not explicitly account for physical degradation laws, the difficulty of capturing instance-specific heterogeneous degradation signatures, and limited characterization of downstream predictive epistemic uncertainty under reconstructed inputs. To address these limitations, we propose a two-stage reconstruction–prognosis framework with uncertainty-aware downstream prediction, integrating two core modules: a Macro-Anchored Physics-Informed Generative Adversarial Network (MAP-GAN) tailored for missing sequence reconstruction, and an Attention-Bidirectional Long Short-Term Memory network integrated with Monte Carlo Dropout (Attention-BiLSTM-MCD) for uncertainty-aware prognosis, which provides an empirical estimate of predictive uncertainty under reconstructed inputs and produces RUL estimates with probabilistic intervals. Specifically, a macro-anchored micro-window generator confines the receptive field to mitigate dimensionality contamination; degradation-informed physical constraints are embedded into the adversarial objective to encourage the synthesized trajectories to satisfy physical degradation constraints; an encoder-guided latent space inversion mechanism adaptively captures individual-specific degradation patterns; and the Attention-BiLSTM-MCD approximates epistemic uncertainty to support prognostic reliability. The effectiveness of the proposed method is examined on a lithium-ion battery dataset with continuous block missingness, and is further verified through missing-pattern sensitivity analyses (missing ratios of 20–60% and early/late missing positions), physics-constraint term-wise ablation with weight sensitivity, an uncertainty-calibration analysis, and an external validation on a second, independent battery dataset.