A New Transient Gas Path Diagnostic Method for Gas Turbine Engines with Multi-timescale Inertial Compensation
Bibliographic record
Abstract
Gas turbine is an advanced power machine commonly used in aviation, Marine and power generation.The existing steadystate diagnosis model is limited to encompassing merely 20% of the flight process for aero gas turbines.To broaden the scope of data available for fault diagnosis, this study introduces a fault diagnosis, considering the dynamic attributes of the system.On the basis of the traditional steady-state model, the dynamic characteristics of the system are considered, including mechanical inertia, volumetric inertia and thermal inertia.A gas turbine model integrating multi-timescale inertial elements is formulated.Within this framework, mechanical inertia is harnessed to determine shaft speed, volumetric inertia is instrumental in adjusting combustor pressure, and thermal inertia is factored in while computing outlet temperature.These multi-time scale inertial features can be obtained from design handbook and experimental data.A comparative evaluation is conducted between the proposed dynamic method and the conventional steady-state approach.the results demonstrate that the multi-time scale inertial compensation model is capable of encompassing the operational data under dynamic conditions.The proposed model yields the anticipated diagnostic outcomes in both normal and faulty operation scenarios.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".