A blade tip-timing method without once-per-revolution sensor for blade vibration measurement in gas turbine engines
Bibliographic record
Abstract
The blade vibration measurement is crucial for gas turbine engines to ensure safe operations. One of the techniques is blade tip-timing (BTT), which is under the assumption that rotor speed is constant and depends on a once-per-revolution (OPR) timing reference to calculate the blade tip displacement and to identify the blade sequence. However, this assumption is incorrect for transient conditions, and the installation of an OPR sensor sometimes is not allowable and reliable. These reasons greatly limit the application of the BTT technique. This paper proposes a self-correcting (SC)-BTT method to realize the blade vibration measurement under different operating conditions without the use of an OPR sensor. The proposed method is based on polynomial fitting, and a reference probe is used to correct high-order fitting coefficients. Numerical results show that the SC-BTT method can greatly reduce the fitting error caused by blade pitch and vibrational parameters. Experimental results demonstrate that the proposed method is capable of removing the limitation of the lack of an OPR sensor and overcoming the drawbacks of the OPR system, such as the failure of the OPR sensor or low-speed resolution. For three investigated cases, the relative errors of derived rotor speed are below 0.12%. The relative error of blade peak-to-peak amplitude (PPA) and initial phase angle are around 3% at the resonance region with engine order (EO) 2.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".