A study on the use of discrete event data for prognostics and health management: discovery of association rules
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 study addresses prognostics and health management (PHM) for manufacturing machines. Different from previous researches where continuous monitoring is assumed for PHM, we investigate the issue with discrete event data. Various event data are recorded during system operation, which can provide useful information for fault diagnosis and failure prediction. We focus on discovery of association rules based on the industrial discrete data. Apriori algorithm is employed to discover the frequent event groups and identify strong association rules. To accommodate the algorithm, the initial event data is transformed into the form of transactional data as a first step. The obtained association rule estimates the occurrence probability of certain significant events within specified time interval. It is concluded through a case study that the number of frequent event groups and strong association rules increases with the time interval that the events are grouped as one transaction.
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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 | 0.001 | 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