An Integrated Fault Detection and Isolation and Safe-Parking Framework for Networked Process Systems
Bibliographic record
Abstract
This work considers the problem of fault detection and isolation (FDI) and fault-handling for networked process systems subject to actuator faults. Multiple units are interconnected in the context of a networked plant. It is assumed that the failed actuator reverts to its fail-safe position and precludes the possibility of nominal operation in the affected unit. First, a robust FDI design is presented, where relations between the prescribed inputs and state measurements in the absence of faults are constructed with the consideration of uncertainty by using the process model. A fault is detected and isolated when the corresponding relation is violated. Then, an algorithm is developed to generalize the safe-parking approach (maintaining the process at an appropriate temporary operating point, which is called a safe-park point, during fault rectification) for fault-tolerant control to account for complex interconnections such as parallel and recycle streams in networked process systems. In particular, it can determine the units that need to be safe-parked during fault rectification and generate possible safe-park points for these units. The efficacy of the integrated FDI and safe-parking framework is demonstrated on a chemical process example comprising three reactors and a separator.
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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 | 0.001 | 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 | 0.001 | 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 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".