Fault prognosis technology for non‐Gaussian and nonlinear processes based on KICA reconstruction
Bibliographic record
Abstract
Fault prediction is desired when a fault is detected for an industrial process, which can greatly enhance the reliability and safety of the overall system. Since independent component analysis (ICA) has been successfully applied to monitor non‐Gaussian processes, it is promising to build a data‐driven prediction technology based on the ICA framework. This paper considers the fault prediction issue with a kernel independent components analysis (KICA) model to deal with both nonlinear and non‐Gaussian features from industrial data. First, the KICA model is used for fault detection and fault reconstruction, so that the magnitude of the fault can be estimated properly. Then, based on the autocorrelation characteristics of the fault magnitude sequence, a multi‐layer hierarchical prediction model is used to predict the trend of the fault. Finally, the effectiveness of this framework is verified on the Tennessee Eastman process.
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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".