Multiple-model based sensor fault diagnosis using hybrid kalman filter approach for nonlinear gas turbine engines
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, an efficient sensor fault detection and isolation (FDI) strategy is proposed based on multiple-model (MM) approach. The scheme is composed of hybrid kalman filters (HKF) by integrating a nonlinear gas turbine engine model that represents the operational engine model with a number of piecewise linear (PWL) models to estimate sensor outputs. The proposed FDI scheme is capable of detecting and isolating permanent sensor bias faults during the entire operational regime of the engine by interpolating the PWL models using a Bayesian approach. Another important aspect of our proposed FDI strategy is its effectiveness within the engine life cycle by periodically updating the model to the degraded health parameters, that one estimated by means of an off-line trend monitoring system that is based on post flight data. The simulation results demonstrate the effectiveness of our proposed online sensor fault diagnosis scheme as well as the robustness of our technique with respect to the engine health parameters degradations.
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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 | 0.000 | 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 it