State-Based Opportunistic Maintenance With Multifunctional Maintenance Windows
Bibliographic record
Abstract
Industrial assets exposed to random environment often exhibit complex deterioration mechanisms with health status variations. In actual field operation, hidden defect signals are usually crucial indicators of upcoming malfunctions and also reminders of proactive maintenance executions. Despite the extensive applications of defect-centered maintenance, in the literature, little attempt has: a) captured the impact of random environments on health variation and restoration, and b) explored the differentiated functions of maintenance windows in separate states. This article addresses these challenges by introducing a state-based maintenance policy with multifunctional maintenance windows. The impact of environmental disturbance on both defect initialization and propagation is characterized by random increment of the state transition rate as well as probabilistic malfunction risk. Three types of maintenance windows (regular, opportunistic, and postponed) are scheduled to ensure a flexible scheduling of inspection and spare part resources. Importantly, the function of opportunistic window is state-based, defect identification when normal and removal when defective. The objective is to minimize the cost rate via the joint optimization of inspection interval, postponed interval, and opportunistic threshold. Experimental studies demonstrate the superior performance of this policy over some conventional policies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".