Independent Component Analysis Mixture Model Based Dissimilarity Method for Performance Monitoring of Non-Gaussian Dynamic Processes with Shifting Operating Conditions
Bibliographic record
Abstract
An independent component analysis (ICA) mixture model based local dissimilarity method is proposed in this article for performance monitoring of multimode dynamic processes with non-Gaussian features in each operating mode. The normal benchmark set is assumed to be from different operating modes, each of which can be characterized by a localized ICA model. Thus, an ICA mixture model is developed with a number of non-Gaussian components that correspond to various operating modes in the normal benchmark set. Further, the Bayesian inference rules are adopted to determine the local operating modes that the monitored set belongs to and the ICA mixture model based dissimilarity index is derived to evaluate the non-Gaussian patterns of process data by comparing the localized IC subspaces between the benchmark and the monitored sets. Moreover, the process dynamics are taken into account by implementing sliding window strategy on the monitored data set. The proposed ICA mixture model based dissimilarity method is applied to monitor the performance of the Tennessee Eastman Chemical process with multiple operating modes and the fault detection results demonstrate the superiority of the proposed method over the conventional eigenvalue decomposition based and geometric angle based principal component analysis (PCA) mixture dissimilarity methods.
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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 | 0.003 |
| 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 | 0.001 |
| 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".