Fault Detection via Autoencoder Latent Space Differences Between Reference Model and the Plant Operation
Bibliographic record
Abstract
Abnormal plant operations are caused by disturbances, process measurement faults, or malfunctioning equipment. Steady-state or dynamic models of the process units are widely available. Since continuous process plants operate under closed-loop control and available plant data often covers a narrow operating window, the process model can generate normal operating data over a wider window to train an autoencoder to represent that data. For deployment in real-time, the plant model accepts process inputs from the plant and calculates outputs; one instance of the autoencoder accepts data from the plant, and the other accepts data from the model. The occurrence of a process fault leads to Differences in the latent space variables of the two instances of the autoencoder, which enables fault detection. Compared to a traditional PCA-based fault detection framework, an autoencoder-based framework can model nonlinear processes, which is not possible by using PCA or dynamic PCA.
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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".