Development of Equipment Failure Prognostic Model based on Logical Analysis of Data (LAD)
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 research develops an equipment failure prognostics model to predict the equipmentâs chance of survival, using LAD. LAD benefits from not relying on any statistical theory, which enables it to overcome the problems concerning the statistical properties of the datasets. Its main advantage is its straightforward process and self-explanatory results.\nHerein, our main objective is to develop models to calculate equipmentâs survival probability at a certain future moment, using LAD. We employ the LADâs pattern generation procedure. Then, we introduce a guideline to employ generated patterns to estimate the equipmentâs survival probability.\nThe models are applied on a condition monitoring dataset. Performance analysis reveals that they provide comprehensible results that are greatly beneficial to maintenance practitioners. Results are compared with PHMâs results. The comparison reveals that the LAD models compare favorably to the PHM. Since they are at their beginning phase, some future directions are presented to improve their performances.
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.
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 | 0.000 | 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 | 0.000 |
| 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