Integrating Production and Predictive Maintenance Planning Model with Industry 4.0 Technologies
Bibliographic record
Abstract
In the present paper, we address the challenge of dynamically integrating production and predictive maintenance planning within the framework of Industry 4.0, employing a rolling horizon approach. Production planning involves determining the optimal levels of production, inventory holding, backorder, and set-up to meet the demand for all products within a defined planning horizon. Preventive maintenance action is employed to replace the system with a new one to ensure optimal performance and reliability. Corrective maintenance is executed to return the system to an “as-bad-as-old” condition when a failure occurs. Minimizing the total costs associated with maintenance and production planning is the objective of the integrated model. Based on having access to the data collected by sensors within the Industry 4.0 framework, a deep learning method is used to predict the system's health condition. Consequently, a Long Short-Term Memory (LSTM) method is utilized to determine the most suitable maintenance action based on the system's condition. By leveraging the rolling horizon approach, the model can simultaneously replan the production and maintenance decisions in real-time by incorporating new obtained sensor data. The numerical example highlights the efficacy of the data-driven integrated model in comparison to the model-based integrated model.
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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 | 0.001 |
| 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 | 0.000 |
| 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".