A new integrated strategy for optimizing the maintenance cost of Production systems using reliability importance measures
Bibliographic record
Abstract
This paper presents an opportunistic condition-based maintenance strategy for multi-component systems. This strategy allows the selection of systems’ components to be replaced or to undergo maintenance repairs based on the effect of importance measure concepts (IMC). IMC models proceeds by determining the contribution of each component in terms of success or failure degrees respecting two factors: (1) the probability of components to remain in operations under various conditions, and (2) the location of such components in the system structure. However, evaluating IMC factors is not so easy for complex systems; it requires knowing their operational structure, their reliability, and the calculation of the IMC degree and ranking them for the selection. Then, the IMC results are integrated into an opportunistic maintenance strategy to constitute a maintenance system's framework. In addition, a multicomponent system composed of 14 nodes and 23 links is provided to illustrate the proposed maintenance strategy that incurs a maximum of cost saving and to reduce spare parts unnecessarily usage.
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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 | 0.001 | 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".