Bibliographic record
Abstract
Since their inception, industries have experienced the negative effects of downtime, lost productivity, lost revenue, as well as layoffs.The prediction of an item's remaining useful life (RUL) enables maintenance techniques to avoid costly and serious damage.As a result, a prognostic is now acknowledged as a crucial activity.Thanks to the Internet of Things (IoT) and IT solutions like Computer Aided Maintenance Management (CMMS) software packages, industries today have a vast amount of data gathered from on-site sensors.This offers real-time data on the equipment's state as well as each piece of equipment's history of interventions from the CMMS software database.By utilizing the vast amount of data that has accumulated over the years, we will be able to extract even more crucial information.The use of artificial intelligence (AI) methods can open up new possibilities for CMMS software packages.In this study, we try to predict RUL (Remaining Useful Time) using an artificial intelligence technique called association rules.This strategy is applied to enhance existing CMMS software programs.Experiment is carried out with a well-known dataset provided by the NASA Ames Research Center and the CoE "Center of Excellence".Experiment results indicate that our suggested approach performs well in forecasting the RUL of turbojet engines and that it also significantly improves the outcomes of predictive maintenance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".