Analyzing and Predicting Overall Equipment Effectiveness in Manufacturing Industries using Machine Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper investigates the use of machine learning algorithms to derive an approximated metric model for predicting the Overall Equipment Effectiveness (OEE) from an industrial process. Analyzing this information, it is possible to ensure better understanding of the business and stimulating the search for improvements of the productive efficiency in industries. In this context, our objective is to explore and apply different ML techniques (supervised and unsupervised learning algorithms) to derive an approximated metric for estimating the overall efficiency in a production line using historical data (dataset) obtained from actual machines in a factory. By using the manufacturing data of a product and specific learning algorithms, a prediction model is created to identify the ideal OEE metric, indicating that the equipment will be used according to its capacity and productive efficiency. Thus, we are able to predict the OEE of a given machine, and to analyze the behavior obtained in order to improve production. From the learning OEE metric, it is possible to analyze the equipment behavior and verify the existence of some patterns which could be used to propose improvements in the manufacturing process. Experimental results have demonstrated the feasibility and evaluation of the proposed models for verifying the efficiency of the industrial plant for different business standards.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it