Asset-criticality-guided optimization for rotating machinery maintenance decision-making considering the benefits of using prognostic techniques
Bibliographic record
Abstract
Advancements in monitoring techniques have facilitated the use of prognostic information to evaluate the health status of rotating machinery, enabling proactive mitigation of potential failures and thereby reducing maintenance costs in manufacturing systems. However, the application of prognostic techniques incurs substantial costs, mainly attributable to sensor acquisition, scheduled replacement, and reinstallation requirements. Consequently, the economic trade-offs of implementing prognostic techniques for continuous condition monitoring remain insufficiently explored in existing maintenance strategies. To bridge this gap, a novel asset-criticality-guided maintenance strategy is developed to maximize the expected revenue of manufacturing systems. Compared to reported works, three key contributions are made. First, the proposed maintenance strategy incorporates machine criticality as a key decision variable within the optimization framework, utilizing actual maintenance records to inform maintenance decision-making. Second, the proposed decision-making model addresses the critical challenge of identifying specific assets for continuous monitoring to maximize net revenue. Third, the strategy details component-level repairs for diverse failure modes within a degraded working efficiency model. This framework enables probabilistic assessment of policy benefits and quantifies uncertainty in maintenance planning. A numerical example and a real-world case study from a pulp mill are provided to demonstrate and validate the proposed method.
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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 | 0.001 |
| 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) | 0.002 | 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".