Fault classification based on variable‐weighted dynamic sparse stacked autoencoder for industrial processes
Bibliographic record
Abstract
Abstract Effective process monitoring and fault diagnosis are of great significance to the safe operation of industrial processes as production scale increases and production systems become more and more complex. Identifying fault types in complex industries by fault classification can help workers to determine the source of the fault as soon as possible, which is crucial to timely recover work. In this paper, a fault classification method based on a variable‐weighted dynamic sparse stacked autoencoder is proposed. First, considering the dynamic characteristics of process data, the input data are processed dynamically by a sliding window. Then, in the pre‐training stage, the weight of each variable is calculated by Fisher discriminant analysis for the reconstruction of the loss function. The sparse term is added to the loss function so that it can learn the effective representation of data in a harsh environment. Finally, the proposed method is applied to the Tennessee‐Eastman benchmark to evaluate the classification performance. The result shows the superiority of the proposed method.
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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 | 0.001 |
| 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".