An Availability-Constrained Integrated Maintenance–Monitoring Model for a System With Failures Following an NHPP
Bibliographic record
Abstract
This article presents an integrated model for production equipment maintenance and online process monitoring when the assignable causes and the equipment failures come from a nonhomogeneous Poisson process. To this end, six possible scenarios within a production cycle are described. These scenarios are defined based on equipment failures and control chart signals (true or false) within a production cycle and process condition at the end of cycle. Then, the occurrence probability and the expected time and cost of each scenario are calculated. The proposed model is characterized by five decision parameters, including number of inspections until planned maintenance, time interval between consecutive inspections, sample size, control limit coefficient, and optimal planned maintenance time. Moreover, the long-run expected cost rate is used as the objective function of the optimization problem, and two sets of constraints have been considered. The former set stands for statistical design of control chart, and the latter is related to equipment availability. Finally, a comprehensive numerical analysis is conducted to assess the sensitivity of the model and to compare the performance of the proposed integrated model to a stand-alone planned maintenance model. The results of the comparative study show that the integrated model outperforms the corresponding stand-alone planned maintenance model. The proposed policy is illustrated using a case study in a food production process.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".