A <scp>multi‐fault</scp> diagnosis method based on improved <scp>SMOTE</scp> for <scp>class‐imbalanced</scp> data
Bibliographic record
Abstract
Abstract With the development of industrial processes, how to effectively diagnose the faults in an increasingly complex production process has attracted widespread attention. It is worth noting that there may be multiple types of faults in the actual industrial process, and there is an extreme class imbalance between the normal samples and the fault samples. Therefore, it is of practical significance to carry out research on the multi‐fault diagnosis method for class‐imbalanced data. In this paper, a multi‐fault diagnosis method based on improved synthetic minority sampling technology (SMOTE) is proposed. First, aiming at the class imbalance, an improved SMOTE algorithm based on Mahalanobis distance (Mahalanobis distance‐based SMOTE [MSMOTE]) is proposed for oversampling. As the Euclidean distance in the traditional SMOTE algorithm does not consider the coupling relationship between features, the Mahalanobis distance is introduced, which is not dependent on the scale and eliminates the influence of different dimensions. Second, in order to better obtain the global and local information of the sample, the kernel local Fisher discriminant analysis (KLFDA) algorithm is used for feature extraction. Third, a multi‐fault diagnosis model based on the AdaBoost.M2 classifier is constructed in which the decision tree is introduced as the weak classifier. The Adaboost.M2 algorithm integrates multiple decision trees by setting the sample weight, the label weight, and the classifier weight, which effectively improve the classification accuracy by only using the decision tree. Finally, the Tennessee Eastman process is used to conduct case studies. For the comparison results, the proposed multi‐fault diagnosis method based on improved SMOTE has higher accuracy and F1‐Score.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".