Temperature measurement uncertainty quantification in condition monitoring of critical infrastructure using complex timeseries dependency modeling
Bibliographic record
Abstract
Maintenance interventions are required to keep power generation component temperatures within prescribed guidelines but come with the consequence of lost generation days. Understanding temperature increases caused by asset aging processes is critical to maintain safe operation but avoid needless maintenance. This is particularly important when power plants are approaching the end of their planned operational lifetime and may not operate as efficiently, eroding generation revenue margins. Temperature measurements, in many cases the earliest indicators of performance degradation, can be subject to a variety of uncertainty and noise stemming from plant configuration, sensor calibration changes and the general variability of component aging processes. The capability to provide confidence bounds on the predicted temperatures in the presence of measurement noise can permit maintenance decisions to be made with sufficient certainty on lead time to select the best course of maintenance action, given operational or financial constraints. This paper presents an approach for identifying the rate at which mechanical component temperatures can increase over a given operational horizon and presents a predictive distribution of the predictive error that may result from that estimate. A framework utilizing the dependency structure between propagated measurement and modeling uncertainty is developed through investigating a series of increasingly detailed Copula-based approaches applied to the residuals from data-based predictive models. The contribution is demonstrated on operational power generation data as well as stylized exemplar data. • Understand temperature measurement uncertainty propagation in lifetime prediction. • Provides temperature prediction confidence in presence of noise and errors. • Improved confidence by complex dependency between time measurement and uncertainties • Copula approach demonstrates improved performance on actual operational asset data
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".