Data Analytics for Performance Monitoring of Gas Turbine Engine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Performance analysis of a low power rating and partially loaded industrial gas turbine engine (GTE) was carried out by using a model-free data analytic approach. By adopting an efficient input selection method, several performance indices (PI) are proposed to quantify the performance of the GTE. These indices are extracted using engine operating data related to power output and parameters related to fuel consumption, and validated with engine performance monitoring measurements for a three year period corresponding to one time between overhaul intervals. The dependency of the PIs on ambient temperature has been studied by using linear and polynomial fitting curves. Then novel methods are introduced for analysis of short-term and long-term performance deterioration arising from compressor fouling and structural degradation respectively. The results have clearly shown the ability of the proposed PIs to detect short-term compressor fouling as well as long-term performance deterioration, which is directly relevant to the Prognostics and Health Management of gas turbine engine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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