Complex multi‐stage process fault detection based on t‐ <scp>SNE</scp> ‐ <scp>BPNN</scp> combined with spatiotemporal neighbour center distance
Bibliographic record
Abstract
Abstract To address the difficulty of fault detection in nonlinear, dynamic, and multi‐stage processes, a spatiotemporal neighbour centre distance (SNCD) statistic is proposed. SNCD is combined with t‐distributed stochastic neighbour embedding (t‐SNE) and back propagation neural network (BPNN) to develop the t‐SNE‐BPNN‐SNCD (tB‐SNCD) fault detection method. The t‐SNE‐BPNN leverages BPNN to learn the nonlinear implicit mapping relationships during the t‐SNE feature extraction and dimensionality reduction process, solving the problem of embedding new samples in t‐SNE. SNCD utilizes not only the spatial neighbour information of samples but also their temporal neighbour information, providing a more comprehensive extraction of process features, eliminating the autocorrelation of process data, and overcoming the difficulties posed by the dynamics of the process for fault detection. Since SNCD makes decisions based on the neighbourhood of samples, it is applicable to nonlinear, multi‐stage processes. The performance of tB‐SNCD is tested through numerical simulation processes and the Tennessee Eastman process, showing a higher fault detection rate compared to KPCA, DPCA, DKPCA, KNN, PC‐WKNN, and LOF methods. Particularly, when faults are time‐related, the fault detection rate of tB‐SNCD is significantly higher than that of classical 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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".