Condition-based Maintenance Optimization of Degradable Systems
Bibliographic record
Abstract
This paper develops a mathematical model for condition-based maintenance optimization of multi-state systems. The majority of the existing literature on maintenance optimization assume that there is no additional cost incurred because of side effects of equipment degradation. Nevertheless, as the operating cost increases with equipment age and degradation, it is important to consider the degradation side effects in the maintenance decision-making process. An important feature of the proposed model lies in the fact that it incorporates side effect of degradation process into condition-based preventive maintenance optimization. We develop a continuous-time discrete-state Markov chain model describing the deterioration stochastic process of a single component. The component is modeled as a multi-state system, where each discrete state is characterized by a degradation level. Numerical examples show the importance of considering such side effect costs when optimizing the choice of maintenance policy. The proposed model is extended to deal with multi-state series systems. Using an example of a series system with two components, it is shown that preventive maintenance and side effect costs should not be optimized for each component individually, but from the perspective of the series system as a whole.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".