Fault Detection and Identification for Sensor Channels in Steam Generator Level Control Loops
Bibliographic record
Abstract
Faults in sensor channels as a result of calibration errors or a bias in transmitters can lead to various safety and operational issues in nuclear power plants, such as reduced trip margins, jeopardized performance, or creating a false sense of security regarding the health of the plant operation. Unfortunately, such faults are difficult to detect, specifically when the sensors are part of a closed-loop system, which has a tendency to reduce or to mask the effects of the fault through feedback control actions during a dynamic transient process. Detecting these types of faults within a steam generator (SG) level control system is investigated in this paper. It is shown that the tools based on analytical redundancy can be very effective in this case. Using a typical SG level control system as a reference plant, specific analytical redundancy formulations are derived. Three sensor fault scenarios have been considered in detail. It has been shown, by simulation and analysis, that the proposed technique can effectively detect the sensor faults that are otherwise difficult to detect.
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".