Fault diagnosis of industrial processes using dynamic global–local preserving projection and genetic algorithm‐based feature selection
Bibliographic record
Abstract
Abstract In the realm of industrial production, where the scale is continuously expanding, chemical process variables often exhibit complex characteristics such as nonlinearity, multi‐modality, and dynamic behaviour. Traditional fault diagnosis methods based on multivariate statistics, like principal component analysis (PCA), generally operate under the assumption that current values are independent of historical statistical values. Additionally, most of these fault diagnosis algorithms focus on feature extraction, which, despite reducing the number of features, often results in a loss of the original data's characteristics. To address this issue, the fault diagnosis and monitoring algorithm introduced in this study integrates genetic algorithm (GA)‐based feature selection with dynamic global–local preserving projection (DGLPP). This approach not only accounts for the dynamic nature of multivariate data but also reduces dimensions while retaining the original features of the data. The effectiveness of this methodology is demonstrated through comparative experiments using the Tennessee Eastman process dataset. This paper compares the proposed model with four existing models: dynamic principal component analysis (DPCA), global–local preserving projection (GLPP), DGLPP, and GA‐DPCA and establishes a significant enhancement in performance with the proposed method.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".