Identifying Correlations Between Independent Sets of Maintenance and Manufacturing Quality Data
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
Effective equipment maintenance is essential for a manufacturing plant seeking to produce high quality products. The impact of equipment reliability and quality on throughput have been well established, however, the relationship between maintenance and quality is not always clear or direct. This paper describes a statistical modeling method that makes use of a Kalman filter to identify correlations between independent sets of maintenance and quality data. With such a method, maintenance efforts can be better prioritized to satisfy both production and quality requirements. In addition, this method is used to compare results from the theoretical maintenance-quality model to data from an actual manufacturing system. Results of the analysis indicate the potential for this method to be applied to preventive as well as reactive maintenance decisions since ageing aspects of equipment are also considered in the model.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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