Detection of Sensor Abnormalities in a Pressurizer by Means of Analytical Redundancy
Bibliographic record
Abstract
To improve the reliability of pressurizer level measurement channels against sensor common cause failures (CCFs), a sensor fault detection scheme has been developed based on analytical redundancy in this paper. The fault detection mechanism has been transformed into statistical tests on a Kalman filter generated innovation sequence. An exponentially weighted moving average (EWMA) scheme has been developed to work with other sensor channels to ensure that the abnormality can be detected and isolated as quickly as possible. Because analytical redundancy does not share the same failure modes as existing sensor channels, the overall reliability of the measurement system has been improved. The paper provides a detailed procedure for formulating analytical redundancy, generating the corresponding innovation sequence, and detailing the design and implementation of the proposed detection scheme. Finally, the performance of the proposed scheme has been evaluated in a CANDU (Canada deuterium uranium) pressurizer to demonstrate its potential benefits. The speed of the fault detection is also evaluated against that of the average run length of the Shewhart control chart. It can be concluded that the proposed scheme is significantly more sensitive to sensor abnormalities at an earlier stage in their development. Even though level measurement channels in a pressurizer are considered, the methodology and design techniques can easily be extended to other systems/applications.
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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 | 0.000 | 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".