Fault Detection and Classification for Nonlinear Chemical Processes using Lasso and Gaussian Process
Bibliographic record
Abstract
This paper presents a statistical monitoring methodology to identify and diagnose intermittent stochastic faults occurring in a nonlinear dynamic chemical process. This methodology addresses three important aspects in model-based fault detection and diagnosis (FDD): model simplicity, interpretability, and calibration. The goal is to generate a surrogate model that can be easily interpreted while maintaining model flexibility and efficiency. The key feature is the use of an active set optimization in combination with a Gaussian process (GP) model for fault detection and classification. To optimally select measured variables for inferring faults, an active set optimization with l1-norm regularization is combined with statistical analysis. This can provide a trade-off between model dimensionality and model prediction error. To ensure sufficient data for the calibration of GP models, an improvement in a probability-based model adjustment algorithm is developed. The performance of the developed FDD scheme is illustrated with two examples: (i) a chemical process consisting of two continuous, stirred tank reactors (CSTRs) and a flash tank separator, and (ii) the Tennessee Eastman benchmark problem. In addition, to deal with multiple-root-cause faults, the GP model based classification was investigated. The summary of the results show that the methodology in this work can cope with both individual and simultaneous occurrences of multiple-root-cause faults in the presence of uncertainty.
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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 | 0.001 |
| 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".