Research on dynamic anomaly diagnosis of control loop based on clustering algorithm and t‐ <scp>SNE</scp> visualization
Bibliographic record
Abstract
Abstract Since abnormal working conditions or early equipment failures usually cause abnormal performances of critical control loops, loop abnormality must be detected effectively. Motivated by the intrinsic complexity of control loop dynamics, our study introduces a novel scheme that integrates classification and visualization techniques to delve into their dynamic characteristics. The scheme contains correlation analysis, K‐means clustering, and t‐distribution stochastic neighbour embedding (t‐SNE). Firstly, dynamic characteristics from historical data of both manipulated and controlled variables within the control loop were extracted. Subsequently, the correlation analysis method was employed to identify the finite impulse response model. Secondly, various sets of finite impulse response model parameters were compiled into a data matrix for further analysis using the K‐means algorithm to cluster the data effectively. Next, the dimensionality of the labelled data matrix was reduced using t‐SNE for visualization purposes. The scaling process iterated until distinct boundaries emerged for each category, labelling them based on a predefined threshold. Finally, in the online phase, anomalies are diagnosed using the parameters of the finite impulse response model derived from real‐time data, comparing them with the scaled offline model set. The effectiveness of our scheme is validated through the application of real‐world data from a Chinese refinery to identify anomalies within the control loop. Furthermore, our scheme demonstrates superior accuracy when compared with traditional techniques such as principal component analysis (PCA), isometric mapping (ISOMAP), independent component analysis (ICA), and multiscale wavelet transform.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".