Mitigating Data Scarcity for Satellite Reaction Wheel Fault Diagnosis with Wasserstein Generative Adversarial Networks
Bibliographic record
Abstract
Data scarcity is an issue that has been a deterrent for researchers looking to employ data-driven fault diagnosis and prognosis methods in satellites and spacecraft. With the ongoing boom in artificial intelligence and machine learning, it has become difficult not to notice this field’s significance and potential in the fault diagnosis and prognosis area. Moreover, the surge in the use of small satellites has contributed to the need to devise effective and efficient health monitoring methods. However, not much operational data on satellites’ subsystems is usually available when employing diagnostic data-driven methods. In this study, we propose an approach based on the Wasserstein Generative Adversarial Network (WGAN)s and Long Short-Term Memory (LSTM) networks to 1) mitigate the data scarcity issue by generating diverse datasets from whatever scarce data that is available and 2) perform fault detection and identification on a single satellite reaction wheel. The proposed dual-one-dimensional-WGAN-LSTM model is tested using reaction wheel time-series data and demonstrated successful performance in diagnosing faults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".