Bibliographic record
Abstract
This paper investigates the use of a Kalman filter (KF) to predict, within the shutdown system (SDS) of a nuclear power plant (NPP), whether safety parameter measurements have reached a trip set-point. In addition, least squares (LS) estimation compensates for prediction error due to system-model mismatch. The motivation behind predictive shutdown is to reduce the amount of time between the occurrence of a fault or failure and the time of trip detection, referred to as time-to-trip. These reductions in time-to-trip can ultimately lead to increases in safety and productivity margins. The proposed predictive SDS differs from conventional SDSs in that it compares point-predictions of the measurements, rather than sensor measurements, against trip set-points. The predictive SDS is validated through simulation and experiments for the steam generator water level safety parameter. Performance of the proposed predictive SDS is compared against benchmark conventional SDS with respect to time-to-trip. In addition, this paper analyzes: prediction uncertainty, as well as; the conditions under which it is possible to achieve reduced time-to-trip. Simulation results demonstrate that on average the predictive SDS reduces time-to-trip by an amount of time equal to the length of the prediction horizon and that the distribution of times-to-trip is approximately Gaussian. Experimental results reveal that a reduced time-to-trip can be achieved in a real-world system with unknown system-model mismatch and that the predictive SDS can be implemented with a scan time of under 100ms. Thus, this paper is a proof of concept for KF/LS-based predictive trip detection.
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.
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 | 0.000 | 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 | 0.000 |
| 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".