Interval-Dependent Maintenance Effect Modeling for Optimization of Multiple Preventive Maintenance on a Repairable System: A Virtual Age-Based Approach
Bibliographic record
Abstract
This paper presents a novel framework for optimizing preventive maintenance (PM) intervals under interval-dependent maintenance effectiveness in repairable systems. Traditional PM models often assume constant effectiveness, overlooking the empirical reality that restoration quality varies with the timing of intervention. Using failure and maintenance data from underground mining Load-Haul-Dump (LHD) trucks, we calibrate a virtual-age-based model where restoration effectiveness, denoted as [Formula: see text], is a function of the PM interval [Formula: see text]. System failures are modeled via a nonhomogeneous Poisson process (NHPP), and parameters are estimated through maximum likelihood techniques combined with global optimization algorithms including Genetic Algorithms (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO). Univariate and multivariate sensitivity analyses reveal strong nonlinear and asymmetric relationships between PM intervals and availability, especially for moderate maintenance (Type II). Optimized schedules achieve significantly improved availability compared to OEM policies, and robustness checks show that small deviations from optimal intervals incur only marginal losses, providing operational flexibility. A set of three-dimensional surface plots further illustrates the interaction effects among PM types, while local perturbation analyses quantify local robustness. The proposed methodology enables maintenance planners to jointly evaluate effectiveness and timing, providing a scalable approach to real-world reliability optimization. The findings underscore the importance of interval calibration in maintenance scheduling and offer practical decision support for high-stakes industrial applications.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".