A Multiagent-Based Methodology for Known and Novel Faults Diagnosis in Industrial Processes
Bibliographic record
Abstract
This article proposes a multiagent-based methodology for the real-time fault diagnosis in industrial processes. This articles aims to build a decision support tool that helps process operators identify and better manage abnormal situations. The supervised and semisupervised machine learning methods are widely used to develop such tools. Despite their accuracy in classifying faults, supervised methods have a major limitation: they cannot diagnose novel faults. The semisupervised methods can detect and isolate novel faults but cannot disclose their root causes. The proposed methodology combines both supervised and semisupervised methods in a parallel-serial structure, exploiting their respective strengths. Moreover, it provides the process expert with the meaningful explanations of the detected novel faults or otherwise. Two case studies are used in this article to demonstrate the effectiveness of the proposed methodology. The first case is the Tennessee Eastman process benchmark. The second one uses the real data collected from a heat recovery system in a thermomechanical pulp mill.
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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 | 0.001 |
| 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".