Comparative Assessment of Preventive Maintenance Strategies for Enhanced Reliability, Security, and Cost Reduction in Industrial Systems
Bibliographic record
Abstract
Preventive maintenance (PM) is essential for enhancing reliability, safety, and costefficiency in industrial systems by significantly reducing failure rates through systematic interventions.This paper conducts a comparative analysis of three advanced optimization methods-Simulated Annealing (SA), Genetic Algorithms (GAs), and Ant Colony Optimization (ACO)-to determine their effectiveness in improving PM strategies.Each method is evaluated based on its ability to optimize maintenance schedules, reduce failure rates, enhance system security, and minimize costs.SA demonstrates robustness in solving complex, non-linear problems, balancing risk mitigation and system security.GA excel in optimizing PM schedules, achieving notable reductions in failure rates and maintenance costs.ACO, with its cooperative approach, is highly effective in cost minimization while maintaining competitive reliability outcomes.The study also explores the challenges of imperfect maintenance, where systems are partially restored to an intermediate state, assessing its impact on reliability and operational stability.The findings emphasize that integrating advanced optimization techniques into PM planning significantly mitigates risks associated with imperfect maintenance, ensures efficient resource allocation, and enhances system resilience.This study underscores the critical role of optimized PM strategies in maintaining secure, reliable, and cost-effective industrial operations, providing a foundation for further advancements in maintenance engineering.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".