Task-aware conformal power envelopes for multi-horizon spacecraft monitoring

Reliable spacecraft power monitoring requires uncertainty estimates across forecast horizons and consistency between primitive electrical states and derived power. We present a task-aware conformal framework that selects joint residual geometry according to downstream power-envelope efficiency. A residual iTransformer produced point centres for five primitive electrical states at 1, 5, and 15 min. Candidate geometries were fitted, selected, and calibrated on chronologically separated complete-orbit blocks, and the final evaluation comprised 25,278 forecast origins from the chronologically subsequent July 2023 telemetry. Across three random-seed runs, the task-aware selector chose a joint box, whereas a residual-volume control selected the sample-covariance ellipsoid. Orbit-balanced primitive-state joint coverage was 0.892 (95% complete-orbit bootstrap confidence interval [CI], 0.883-0.899) at the 90% nominal level and 0.947 (95% CI, 0.943-0.952) at the 95% nominal level. At 90% and 95%, simultaneous coverage of both power channels across all horizons was 0.913 and 0.957. Relative to residual-volume selection, the task-aware method reduced normalized power interval score by 34.3% and 30.3%; the paired absolute reductions were 1.522 (95% CI, 1.505-1.540) and 1.552 (95% CI, 1.534-1.570). Power envelopes were computed from synchronized primitive current and bus-voltage sets. For the selected non-negative box, zero algebraic gap and full interval-image containment follow by construction; the empirical analyses quantify coverage and sharpness. The results demonstrate task-aware geometry selection for BUPT-1 multi-horizon power monitoring.

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Publication Details

Journal
PLoS ONE
Published
2026-10-13
DOI
https://doi.org/10.1371/journal.pone.0360910
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Task-aware conformal power envelopes for multi-horizon spacecraft monitoring

Nan Lu, Lehan Cui, Xinian Zhi
PLoS ONE
Fault Detection and Control Systems
article

Task-aware conformal power envelopes for multi-horizon spacecraft monitoring

Nan Lu, Lehan Cui, Xinian Zhi
article en

Abstract

Reliable spacecraft power monitoring requires uncertainty estimates across forecast horizons and consistency between primitive electrical states and derived power. We present a task-aware conformal framework that selects joint residual geometry according to downstream power-envelope efficiency. A residual iTransformer produced point centres for five primitive electrical states at 1, 5, and 15 min. Candidate geometries were fitted, selected, and calibrated on chronologically separated complete-orbit blocks, and the final evaluation comprised 25,278 forecast origins from the chronologically subsequent July 2023 telemetry. Across three random-seed runs, the task-aware selector chose a joint box, whereas a residual-volume control selected the sample-covariance ellipsoid. Orbit-balanced primitive-state joint coverage was 0.892 (95% complete-orbit bootstrap confidence interval [CI], 0.883-0.899) at the 90% nominal level and 0.947 (95% CI, 0.943-0.952) at the 95% nominal level. At 90% and 95%, simultaneous coverage of both power channels across all horizons was 0.913 and 0.957. Relative to residual-volume selection, the task-aware method reduced normalized power interval score by 34.3% and 30.3%; the paired absolute reductions were 1.522 (95% CI, 1.505-1.540) and 1.552 (95% CI, 1.534-1.570). Power envelopes were computed from synchronized primitive current and bus-voltage sets. For the selected non-negative box, zero algebraic gap and full interval-image containment follow by construction; the empirical analyses quantify coverage and sharpness. The results demonstrate task-aware geometry selection for BUPT-1 multi-horizon power monitoring.

PLoS ONEVol. 21(10)
Northwestern Polytechnical University (CN)
Ministry of Industry and Information Technology of the People's Republic of China, Ningbo Municipal Bureau of Science and Technology
Openalex Percentile: Top 17%
Fault Detection and Control Systems
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