Condition Based Maintenance in Nuclear Power Plants: Limitations & Practicality
Bibliographic record
Abstract
Advancements in sensor and IoT technology and Deep Learning algorithms has made Condition Based Maintenance (CBM) a promising maintenance strategy for many industries. However, the use of these technologies can be limited by safety and cost requirements; more so for a highly regulated nuclear industry. CBM in nuclear powerplants can serve to alleviate maintenance bottlenecks such as providing condition analytics between inspection periods and provide better prognostics. Yet, there are many challenges in trying to implement such a system due to limitations imposed from rigorous safety practices and cost considerations, ultimately affecting the practicality. In this paper, we attempt to address the limitations by undertaking a survey of literature to identify such limitations as well as to aid decision makers in the development and implementation of a CBM system in nuclear power plants. By identifying these limitations, it can also aid in the development of more advanced methods of CBM applicable for nuclear power plants.
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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".