Fleet-Wide Interval-Dependent Nonparametric Modeling and Optimization of Multi-Level Preventive Maintenance Effectiveness: Application to Hybrid (AC) LHD Trucks in a Mine
Why this work is in the frame
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Bibliographic record
Abstract
Preventive Maintenance (PM) policies for repairable systems commonly assume constant effectiveness, overlooking how timing impacts restoration. This study models PM effectiveness as an interval-dependent function, [Formula: see text], embedded in a virtual age degradation model with nonhomogeneous Poisson (power-law) failures. Parameters are estimated via global-local maximum likelihood optimization. Using operational data from underground Load-Haul-Dump trucks, we calibrate PM effectiveness curves (three PM types) and simulate availability under practical interval constraints. The optimized fleet-wide schedule shortens light/moderate PM intervals and modestly adjusts major PM, delivering an 8% increase in long-run availability far above OEM baselines. Sensitivity analysis shows nonlinear, asymmetric responses of availability to interval changes. Perturbation tests show that minor deviations from the optimized intervals incur only marginal losses, indicating operational robustness. This data-driven, interpretable framework enables maintenance planners to jointly optimize timing and effectiveness for enhanced reliability in industrial fleets.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 it