Performance-Based Gas Turbine Health Monitoring, Diagnostics, and Prognostics: A Survey
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
Health monitoring is an essential part of condition-based maintenance and prognostics and health management for gas turbines. Various health monitoring systems have been developed based on the measurement and observation of the fault symptoms including turbine performance parameters such as heat rate, and nonperformance symptoms such as structural vibration. This paper focuses on surveying state-of-the-art condition monitoring, diagnostic and prognostic techniques using performance parameters acquired from gas-path data that are mostly available from the operating systems of gas turbines. Performance parameters and the corresponding effective factors are presented in the beginning. Structure of performance monitoring and diagnostic systems are systematically laid out next, and the recent developments in each section are surveyed and discussed. Observing the importance of the prognostics in the recent trend of health monitoring research, an emphasis is given on the prognostic frameworks and their implementation for the remaining useful life prediction. A conclusion along with a brief discussion on the current state and potential future directions is provided at the end.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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