Nonlinear Fault Diagnosis of Jet Engines by Using a Multiple Model-Based Approach
Bibliographic record
Abstract
In this paper, a nonlinear fault detection and isolation (FDI) scheme that is based on the concept of multiple model (MM) approach is proposed for jet engines. A modular and a hierarchical architecture is proposed which enables the detection and isolation of both single as well as concurrent permanent faults in the engine. A set of nonlinear models of the jet engine in which compressor and turbine maps are used for performance calculations corresponding to various operating modes of the engine (namely, healthy and different fault modes) is obtained. Using the multiple model approach the probabilities corresponding to the engine modes of operation are first generated. The current operating mode of the system is then detected based on evaluating the maximum probability criteria. The performance of our proposed multiple model FDI scheme is evaluated by implementing both the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Simulation results presented demonstrate the effectiveness of our proposed multiple model FDI algorithm for both structural and actuator faults in the jet engine.
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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".