Analysis of Reliability of Fueling Machine Head Encoders
Bibliographic record
Abstract
The objective of this thesis is to investigate in-service performance and reliability of four principal encoders used to guide the motion of the fueling machine (FM), which is used to support the fueling operation in a CANDU nuclear power reactor. \nA primary function of an encoder is to track the precise position of a fueling component, such as the charge tube and ram. However, the tracking of position is occasionally prone to error due to faults in the encoder operation. The sequence jump is such a fault in which the encoder output about the position of a component suddenly increases by a large magnitude in a spurious manner. The sequence jump error is normally a result f unavoidable noise and disturbances in electric circuits and buses connecting the encoder with the fuel handling computers. In such case, the encoder functionality is restored by simple fault recovery process. However, the sequence jump error is also triggered by mechanical faults, such as worn tracking gears or faulty bit-readings. In such cases, the sequence jump error continues to occur so frequently that fueling operation is significantly interrupted. This prompts the replacement of the encoder via a maintenance outage, which also costs resources and lost power generation. \nAt present, there is no capability to predict the reliability and lifetime of encoders as well as no health monitoring strategy. This study aims to tackle these challenges by investigating the three particular aspects of encoder performance: (1) estimation of the lifetime distributions, (2) stochastic modelling of the occurrences of encoder errors, and (3) analyzing the bit patterns of encoder sequence jump errors for health monitoring purposes. This study is based on about 20 years of historical operating data related to these encoders from a nuclear station in Canada. \nThe encoder lifetime distributions are estimated using the lifetime histories collected from the plant maintenance database. These distributions are used to estimate the Mean Time To Failure (MTTF) and mission reliability over an operating interval. The stochastic process modelling and probabilistic bit pattern assessment during a sequence jump error helps to formulate a health monitoring strategy for encoders. \nThe models developed in this research will help to improve reliability of fueling operation, and reduce the unavailability and generation losses caused by abrupt encoder failures.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".