Neural Network-based Fault Diagnosis of Satellites Formation Flight
Bibliographic record
Abstract
The main objective of this thesis is to develop a methodology for detecting and isolating faults (i.e. fault diagnosis) in any of multiple reaction wheels that are commonly employed as actuators in a consensus-based virtual structure controlled formation of satellites. In order to accomplish this objective, a two-level fault diagnosis system is developed based on Dynamic Neural Networks (DNNs). In the lower-level of the formation flight system hierarchy, a local fault diagnosis module is available in each individual satellite. In this level, the fault diagnosis system may consist of a dynamic neural network that is trained by using absolute measurements and states of each \nsingle satellite. Unfortunately, a local fault diagnosis system may fail to detect the presence of low severity faults. In an individual satellite these low severity faults \nmay not cause any serious complications with the specifications of the overall mission, however they can cause significant impact on the satellite’s attitude or rates in a given precision formation flight of a network of satellites. Consequently, in order to detect these low severity faults a fault detection system is required to be designed and developed at the higher-level or the formation-level of the mission hierarchy. Towards this end, the highly nonlinear dynamics of the formation flight and the reaction \nwheels are modeled by using dynamic multilayer perceptron neural networks. The proposed formation-level DNNs invoke the extended back propagation learning algorithm and are trained based on sets of input/output data that are collected from the relative attitude determination sensors of the 3-axis attitude control subsystems of the satellites. The DNN parameters are adjusted to minimize certain performance indices (representing the output estimation errors). \nThe capabilities of the proposed DNNs are investigated under various faulty situations, including single and multiple actuator fault scenarios and under high severity and low severity faulty situations. Using a Confusion Matrix evaluation method, it is demonstrated that by using the proposed fault detection and isolation (FDI) scheme, one can achieve a high level of accuracy and precision in detecting faults. The proposed formation-level FDI system has capabilities in efficiently detecting and isolating \nactuator low severity faults simultaneously.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".