Dynamic mutual information similarity based transient process identification and fault detection
Bibliographic record
Abstract
Industrial process status can be modified according to continuous operations, which are required by different production specifications. Commonly, in a multimode process, attention was always paid to stable modes, while transitions were neglected. In a transition process, the process may be externally time varying or nonstationary so that the identification and the modelling are intractable to implement using traditional statistical methods. In this article, a transition identification and process monitoring method is proposed to handle the above problems based on a novel dynamic mutual information similarity (DMIS) analysis. Firstly, a multimode process is represented by a series of overlapping moving windows to consider the local information. In order to extract the corresponding dynamic information, each of these windows is modelled using the dynamic partial least squares (DPLS) method. Then, the mutual information algorithm is introduced to calculate the similarity between different latent variables. The hierarchical clustering method is employed to transfer the similarity information into a visualized dendrogram where the whole process, including a transition, is divided into several segments. The statistical characteristics in each segment are relatively stable and can be characterized by conventional multivariate statistical process control methods. Online identification and fault detection are then carried out for the multimode process through a series of DPLS models, established in the offline steps. The feasibility and effectiveness of the proposed method are validated by the Tennessee Eastman (TE) benchmark and a real process. The results of the proposed methods have shown superior performance compared to previous works.
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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".