Intelligent online process fault diagnosis through integrating Andrews function, autoencoder, and neural networks
Bibliographic record
Abstract
Abstract This paper proposes an online process fault diagnosis scheme that integrates principal component analysis, Andrews function, autoencoder, and multilayer feedforward neural network to enhance the fault diagnosis performance. Useful features are extracted from the online monitoring data by using Andrews function (also known as Andrews plot). To overcome the influence of the arrangement order of variables on the outcomes of Andrews function, a principal component analysis (PCA) model is developed from the normal process operation data. The principal components of the original process data, arranged in the descending order of data variation explained, are used in Andrews function calculation. To address the issue of feature selection in Andrews function, a large number of Andrew function outputs are retained and then compressed using an autoencoder. The compressed features from the encoder are then utilized as the inputs to a multi‐layer feedforward neural network for fault diagnosis. The proposed method is demonstrated on a simulated continuous stirred tank reactor (CSTR) and the diagnostic performance is compared with those of a conventional neural network and an Andrews function based online fault diagnosis schemes. A wide range of process faults in abrupt and incipient fault forms are tested. The results demonstrate that the proposed online process fault diagnosis scheme gives improved fault diagnosis speed and reliability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".