Performance Assessment of Static and Adaptive Maintenance Strategies Under Stochastic Conditions with Penalty-Augmented Objectives Using a Monte Carlo Simulation Approach to Reliability and Cost Optimization
Bibliographic record
Abstract
Condition-based maintenance (CBM) has become a cornerstone for optimizing the performance, reliability, and cost-efficiency of industrial systems.Traditional CBM strategies, which rely on fixed inspection intervals and static preventive maintenance thresholds, often fail to account for the stochastic nature of degradation processes and the inherent imperfections in condition monitoring.These limitations can result in suboptimal maintenance decisions, such as excessive preventive interventions or missed failures.To address these challenges, this study presents a Monte Carlo simulation framework designed to evaluate and compare static and adaptive CBM strategies under conditions of uncertainty.The framework integrates a Wiener-process degradation model, imperfect condition monitoring, adaptive inspection scheduling based on residual useful life (RUL) estimates, and dynamically recalibrated preventive maintenance thresholds.Three maintenance strategies are examined: (i) a static policy with fixed inspection intervals and constant preventive thresholds, (ii) a semi-adaptive policy with fixed inspections and adaptive preventive thresholds, and (iii) a fully adaptive policy with both dynamic inspection intervals and adaptive preventive thresholds.The performance of these strategies is assessed using a comprehensive set of metrics, including total cost, downtime, reliability, short-horizon risk, and a penalty-augmented objective function that integrates cost, reliability, and risk exposure.
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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) | 0.001 | 0.000 |
| 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) | 0.001 | 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".