Multi-Dimensional Time-Series Shapelet Based Real-Time Fault Detection and Localization on ISS Electrical Power Distribution System
Bibliographic record
Abstract
International space station (ISS) is a grand invention for human beings to have a chance at exploring the outer space. Its operation is completely dependent on the autonomous power distribution system which transforms energy by solar arrays from the sun. There is a high demand for a reliable monitoring system that can accurately and timely detect and localize faults in its power system for the special working environment of the ISS. In this paper, a fault detection and localization (FDL) based on multi-dimensional time-series trend extracted shapelet (MTES) method was proposed. A fast shapelet discovery was created to accelerate the process of extracting shape features from time-series signals collected from the ISS electrical power distribution system (EPDS). Then the techniques of randomization and information gain were exploited for the further shapelet selection. Finally, multi-dimensional time-series classification for FDL was solved by a designed random forest classifier. The real-time FDL measurement instrument was emulated on the Xilinx VCU128 FPGA board, while a hardware-in-the-loop (HIL) testing platform was established to verify the effectiveness, execution speed, and accuracy of the MTES method. Comparing with other state-of-the-art data-driven methods, higher accuracy (above 96%) and easier hardware implementation were achieved using MTES
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".