Joint Kalman Filtering and Recursive Maximum Likelihood Estimation Approaches to Fault Detection and Identification of Boeing 747 Sensors and Actuators
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Bibliographic record
Abstract
In this paper, a joint state and parameter estimation scheme is applied to address the problem of detection and identification of loss of effectiveness faults in both sensors or the actuators of a Boeing 747 longitudinal model. The Kalman filter and the recursive maximum likelihood schemes are used for the state and the parameter estimations, respectively. Compared to the other simultaneous state and parameter estimation methods, the proposed strategy maintains the linearity of the system and can also be applied to both sensor or actuator faults. In simulation studies conducted, our proposed approach is compared to the adaptive structure multiple-model scheme. In view of the computational resources considerations, the method proposed in this paper is more efficient than the adaptive structure multiple-model technique and also has the potential to detect and identify faults with lower severities as well as concurrent faults.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it