Fault diagnosis based on the integration of exponential discriminant analysis and local linear embedding
Bibliographic record
Abstract
Industrial process data have the characteristics of high dimensions and nonlinearity, so it is very important to extract the data features for fault diagnosis. Two kinds of improved exponential discriminant analysis methods, local linear exponential discriminant analysis (LLEDA) and neighbourhood preserving embedding discriminant analysis (NPEDA), are proposed and the fault diagnosis schemes based on the two methods are given. The two methods both combine the global discriminant analysis with the local structure preserving. LLEDA is a parallel strategy to find a trade‐off projection vector between the local geometric structure preserving and the global data classification. NPEDA is a cascaded strategy whose dimensionality reduction process is implemented in two serial steps. The two methods emphasize the intrinsic structure of the data while utilizing the global discriminant information, so they have better discrimination power than the traditional EDA method. Finally, a typical penicillin fermentation simulation platform and Tennessee Eastman process are used to verify the performance of the proposed methods.
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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".