Condition-Based Maintenance Scheduling Using Probability Distribution Function and Agglomerative Hierarchical Clustering Approaches: AI-Driven Predictive Maintenance Mapping
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
Advanced industries such as wind turbines aim for condition-based maintenance (CBM) to enhance system availability and reach higher revenue. Hence, decision making plays an essential role in optimizing the maintenance planning process and achieving a more reliable system. In this work, a hierarchical decision process using remaining useful life (RUL) information is introduced for the maintenance scheduling of wind farms. Real-time condition monitoring and failure prognosis are conducted through a Bayesian approach to obtain probability distribution functions (PDFs) for the faulty turbines/components and predict their associated RUL. Following that, an augmented probability model is built by adding the RULs to create a feature map for maintenance. Finally, hierarchical clustering is applied to schedule maintenance times by setting certain planning policy rules using a dendrogram chart along with the wind farm information. This work’s main contribution is that the proposed decision process includes PDFs of faulty turbines used to deliver an optimized maintenance schedule based on the suggested probability feature map. In addition, the hierarchical clustering using wind farm information enhances wind turbine availability and reduces maintenance costs. Field data from wind turbines located in Chile confirm the superior performance of the proposed hierarchical decision policy in comparison with the conventional corrective maintenance strategy.
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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 | 0.000 | 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