Reliability Model and State Probabilities of Electrical System, Subject to Multiple Competing Failure Processes
Bibliographic record
Abstract
This paper develops a comprehensive model for evaluating the reliability of an Oil Circuit Breaker (OCB), which is subject to multiple competing failure mechanisms throughout its service life.Three independent degradation processes are considered: mechanical wear of the circuit breaker contacts due to frequent switching operations, aging and degradation of the insulating oil influenced by thermal and electrical stresses, and random external shocks resulting from electrical faults or mechanical solicitations.Particular emphasis is placed on the design complexity of the OCB, its critical failure modes, and the feasibility of modelling simultaneous degradation effects.The degradation evolution is described using two continuous probabilistic models suitable for electrical power components.These models enable the prediction of the component's degradation states under varying operating environments, taking into account electrical, thermal, and ambient factors.The proposed framework allows for the estimation of both the time-dependent reliability and the degradation state probabilities of the OCB during a defined mission period.Additionally, the model can be used as a decision-making tool for preventive maintenance planning and lifecycle management, thus improving system safety and operational continuity.This work provides a solid foundation for further research in reliability modelling of high-voltage switchgear components.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".