Design and Implementation of a Micro-world Simulation Platform for Condition-based Maintenance using Machine Learning Algorithms
Bibliographic record
Abstract
This thesis explores the development of the simulation platform ARDAS (Automated Reliability Decision Aid System) following the micro-world framework. ARDAS simulates a decision support system for the maintenance management of a hydraulic process applying the Condition-Based Maintenance method. This platform intends to support decision-making to solve maintenance problems that might affect the performance of the hydraulic process. ARDAS uses an open-source data set collected from a hydraulic process to train a Machine Learning algorithm that estimates the health condition of four components within the hydraulic process. This platform also includes the implementation of a web user interface that presents information about the inner workings of the automated system, process diagram, trend of variables, and contextual information. This platform can be used to aid those who wish to study the effects of an ML-based decision aid system on human performance.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".