<scp>LSTM</scp> ‐ <scp>iRealNVP</scp> : Enhanced industrial process monitoring via improved <scp>RealNVP</scp> flow models and fault‐free samples
Bibliographic record
Abstract
Abstract Industrial process monitoring is the cornerstone of safe, high‐quality, and profitable production. Over the past decade, deep autoencoders have become a popular tool for extracting latent representations from high‐dimensional sensor streams. Yet every autoencoder ultimately learns a mere ‘numerical shadow’ of the data, scattering latent variables in unconstrained spaces. The resulting representations are irregular, discontinuous, and highly sensitive to noise. To address these challenges, we propose LSTM‐iRealNVP–an innovative normalizing flow framework integrating temporal feature extraction module. Our solution introduces three key advancements: (1) A mathematically rigorous invertible block, iRealNVP, replaces traditional autoencoders. By enforcing an explicit Gaussian prior through bijective transformations, it ensures constraints on latent variables. (2) A hybrid encoder merges LSTM dynamics with iRealNVP layers, simultaneously capturing non‐linear temporal dependencies and regularizing the latent space into a smooth, unimodal distribution. (3) An anomaly score derived directly from the exact log‐likelihood employs an adaptive threshold and remains robust across varying operating modes. Extensive validation on two industrial benchmarks confirms the transformative impact of these contributions. On the Tennessee Eastman process, LSTM‐iRealNVP pushes the detection rate to 82.41% while suppressing false alarms to 3.06%. On a full‐scale wastewater treatment plant, the method attains a near‐perfect detection rate of 99.75% at a false‐alarm rate of merely 0.8%, outperforming state‐of‐the‐art monitors. These results demonstrate that principled, likelihood‐driven representations can simultaneously elevate detection sensitivity and operational stability, setting a new performance frontier for industrial anomaly detection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".