Use of A Data-Driven Approach for Time Series Prediction in Fault Prognosis of Satellite Reaction Wheel
Bibliographic record
Abstract
Satellites are complicated systems, and there are many interconnected devices inside a satellite that needs to be healthy to ensure the proper functionality of a satellite. Uncertainty and mechanical failure in different integral parts of the satellite pose the major threat for the satellite to remain fully functional for its expected life span. One of the most common reasons for satellite failure is the reaction wheel (RW) failure. Satellite RW fault prognosis can be formed as a two-step process. In this paper, we study the first step where a data-driven approach is used for forecasting the RW parameters that can be used to predict the remaining useful life (RUL) of a reaction wheel onboard satellite. Autoregressive integrated moving average model (ARIMA) and a type of recurrent neural network (RNN) known as the long short-term memory (LSTM) are used for time-series forecasting in this paper. Both models can predict up to a degree of accuracy, even when limited historical data is available. ARIMA works very efficiently as it can capture a suite of different standard temporal structures in time series. Still, when it comes to accuracy, LSTM provides a better regression outcome for our dataset. The results obtained by the models are very optimistic when model parameters are tuned.
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 | 0.000 |
| Open science | 0.000 | 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".