Application of combined support vector machines in process fault diagnosis
Bibliographic record
Abstract
The performance of Combined Support Vector Machines, C-SVM, is examined by comparing it's classification results with k-nearest neighbor and simple SVM classifier. For our experiments we use training and testing data obtained from two benchmark industrial processes. The first set is simulated data generated from Tennessee Eastman process simulator and the second set is the data obtained by running experiment on a Three Tank system. Our results show that the C-SVM classifier gives the lowest classification error compared to other methods. However, the complexity and computation time become issues, which depend on the number of faults in the data and the data dimension. We also examined Principal Component Analysis, using PC scores as input features for the classifiers but the performance was not comparable to other classifiers' results. By selecting appropriate number of variables using contribution charts for classification, the performance of the classifiers on Tennessee Eastman data enhances significantly. Therefore, using contribution charts for selecting the most important variables is necessary when the number of variables is large.
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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 | 0.000 |
| 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 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".