A satellite fault detection system based on telemetry data using statistical process control and time-domain feature extraction
Bibliographic record
Abstract
• Introduction of a novel fault management system that synergistically integrates Statistical Process Control (SPC) and time-domain methods, enhancing satellite fault detection capabilities. • Demonstration of the integrated approach’s effectiveness using the power subsystem of ManitobaSat-1, a student-led CubeSat mission, successfully diagnosing three critical faults. • The proposed system offers a holistic view of spacecraft health by simultaneously capturing statistical variabilities and temporal deviations in system parameters, improving both reliability and accuracy. • Computational efficiency: Unlike traditional machine learning algorithms, the new system is based on mathematical and statistical algorithms that are less resource-intensive, making it suitable for real-time operations and smaller missions. • The model is designed to be scalable and universally applicable, thereby extending its utility from student-led initiatives like ManitobaSat-1 to potentially larger commercial and scientific satellite missions. In spacecraft operations, accurately detecting anomalies in telemetry is essential but often requires complex, time-consuming methods. With the growing number of low-earth orbit missions, there is an urgent need to streamline this process. In this paper, we introduce an efficient real-time fault detection system that specifically addresses three critical faults within a spacecraft’s power subsystem: loss of solar string(s), increase in the battery’s internal resistance, and excessive power consumption. We apply industrial statistical process control and time-domain feature extraction techniques to create algorithms for enhanced fault detection. Our approach involves extensive simulations using a dynamic model of the power subsystem, allowing us to develop a method that is both innovative and practical. This research represents a step forward in the field, as we utilize statistical process control for real-time health monitoring of spacecraft, providing a more efficient and accurate means of analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".