{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T11:04:45Z","timestamp":1790247885524,"version":"4.1.0"},"reference-count":43,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62233017"],"award-info":[{"award-number":["62233017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62373368"],"award-info":[{"award-number":["62373368"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62373369"],"award-info":[{"award-number":["62373369"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62233017"],"award-info":[{"award-number":["62233017"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["62373368"],"award-info":[{"award-number":["62373368"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["62373369"],"award-info":[{"award-number":["62373369"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2013prediction 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\u2013prognosis 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\u201360% 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.<\/jats:p>","DOI":"10.3390\/s26196051","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T10:16:28Z","timestamp":1790244988000},"page":"6051","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Macro-Anchored Physics-Informed GAN for RUL Prediction Under Continuous Block Missing Data"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3298-8600","authenticated-orcid":false,"given":"Yongkang","family":"Peng","sequence":"first","affiliation":[{"name":"Rocket Force University of Engineering, Xi\u2019an 710025, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6678-8297","authenticated-orcid":false,"given":"Jianxun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Rocket Force University of Engineering, Xi\u2019an 710025, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1726-0178","authenticated-orcid":false,"given":"Zhengxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Rocket Force University of Engineering, Xi\u2019an 710025, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5226-9923","authenticated-orcid":false,"given":"Xiaosheng","family":"Si","sequence":"additional","affiliation":[{"name":"Rocket Force University of Engineering, Xi\u2019an 710025, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2452-3538","authenticated-orcid":false,"given":"Dangbo","family":"Du","sequence":"additional","affiliation":[{"name":"Rocket Force University of Engineering, Xi\u2019an 710025, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111120","DOI":"10.1016\/j.ymssp.2024.111120","article-title":"A Review on Physics-Informed Data-Driven Remaining Useful Life Prediction: Challenges and Opportunities","volume":"209","author":"Li","year":"2024","journal-title":"Mech. 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