Fault Diagnosis And Prognosis Of Satellite Attitude Control System With Reaction Wheels and Control Moment Gyros
Bibliographic record
Abstract
Condition-based maintenance (CBM) and prognostics and health management (PHM), as consisting parts of diagnosis, prognosis, and health monitoring (DPHM) framework, have developed over the past decades to remedy the limitations of the traditional maintenance practices for complex systems. In space, where mass and power budget are restricted, application of CBM and PHM has become more vital to the success of a mission. Reaction wheels (RW) and Control Moment Gyros (CMG), as the most commonly used actuators onboard satellites, are prone to faults and failures. The ability to detect faults, isolate their location and severity, and estimate the remaining useful life (RUL) of the faulty unit can enhance mission success rate and reduce maintenance and damage costs extensively. Therefore, in this thesis, a model-based DPHM framework is developed and evaluated. Firstly, a novel fault detection algorithm is proposed, using Unscented Kalman filters (UKF) in conjunction with residual and innovation sequences, for detecting agile faults in RW/CMG onboard satellites. Secondly, a novel fault isolation algorithm is proposed, using UKF, Bayes’ probability and interacting multiple models (IMM), to isolate the location of the fault and its severity. Finally, a new fault prognosis approach is proposed, using UKF and particle filters (PF) to estimate the RUL of a faulty unit. Extensive simulations were conducted for each phase of the DPHM to verify advantages of the proposed techniques over the available methods in the literature. Extensive simulations were conducted to evaluate the performance of the proposed methods in each module of the framework. Regarding the proposed fault detection scheme, results showed superior performance of the proposed adaptation technique compared to the original UKF and a previously developed AUKF. The proposed fault isolation scheme was able to successfully isolate the faulty unit at multiple levels of isolation including formation level, system level, and actuator level with over 99% success rate for formation level, over 99% success rate for the RW assembly and for up to 90% success rate for the CMG assembly in the system level. For the CMG assembly, due to direct estimation of the fault parameters, it was possible to determine the severity of the faults as well as their location. Finally, the proposed fault prognosis approach provided RUL estimates with errors as low as 1.5% compared to the actual remaining useful life. Overall, the proposed framework can be regarded as a promising tool for fault detection, isolation and identification, and prognosis of the complex nonlinear systems. Furthermore, the proposed framework can be extended to other complex systems in space including multi-agent formation systems and other areas where the model of the system under study is available.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".