Classification Predictive Maintenance Using XGboost with Genetic Algorithm
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 develops a condition classification system of compressor 103J and water pump systems which are key equipment in the ammonia production line, hence the monitoring of these two very important machines. In recent years, there are many good intelligent machine learning algorithms and XGboost is one of them. However, it contains many parameters and classification performance of the model will be greatly affected by the selection of parameters and their combination technique. In this paper, XGboost algorithm is combined with the genetic algorithm, called GA-XGboost, in order to find the best hyper parameters of classifiers which makes the classifier more efficient and ensures the proper functioning of compressor 103J and water pump systems. Experiments show that GA-XGboost algorithm has improved the accuracy of classification in the compressor 103J and the water pump dataset compared with other machine learning algorithms like Support Vector Machine (SVM), Random Forest (RF) and AdaBoost. Also experiments demonstrate the improvement of the GA-XGboost algorithm by the combination of different selection and crossover operators of the genetic algorithm.
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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