Importance of Physics-Based Prognosis for Improving Turbine Reliability: RRA 501KB Gas Turbine Blade Case Study
Bibliographic record
Abstract
Prognosis and health monitoring (PHM) technology needs to be developed to meet the challenges posed by aging gas and steam turbines in power plants, transportation systems, gas pipelines, and other industries. It is necessary to use physics based residual life prediction and life extension techniques to take into account the state of damage due to prior service. This paper focuses on the requirements of the technology and the state of the development to date. In this study, Life Prediction Technologies Inc.’s (LPTi’s) prognosis tool known as XactLIFE™ was successfully used to establish the fracture critical location of RRA 501KB first stage gas turbine blades under steady state loads and to compute the average life to creep crack initiation in the blade airfoils. The analysis used typical engine operating data from the field in terms of engine speed and average turbine inlet temperature (TIT). The blade is known to suffer airfoil untwist and lengthening during service and this is obviously followed by stress rupture failure. The primary objectives of the case study are to show how prognosis can allow a user to predict fracture critical locations to avoid failures.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".