Risk-based maintenance and remaining life assessment for gas turbines
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
Purpose – The purpose of this paper is to propose a quantitative model for risk-based maintenance and remaining life assessment for gas turbines. Design/methodology/approach – The proposed model uses historical failure and repair data from the operation of gas turbines. The time to failure of gas turbines is modeled using Weibull distribution. Findings – The total risk is estimated considering replacement cost, repair cost, operation cost, risk of failure and turbine remaining value after a specified period of time. Originality/value – The model is an effective tool to make optimal decisions regarding maintenance strategy (repair or replacement) and to assess the remaining life based on a comparison of the total risk. The literature review focusses on developing different models to make risk-based decisions regarding the selection of a maintenance strategy and maintenance interval, however, literature is silent regarding risk-based assessment of the equipment remaining life, which is the focus of present work. The model is tested and applied to ageing gas turbines in a cross-country pipeline.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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