Distributed output‐feedback fault detection and isolation of cascade process networks
Bibliographic record
Abstract
Distributed output‐feedback fault detection and isolation (FDI) of nonlinear cascade process networks that can be divided into subsystems is considered. Based on the assumption that an exponentially convergent estimator exists for each subsystem, a distributed state estimation system is developed. In the distributed state estimation system, a compensator is designed for each subsystem to compensate for subsystem interaction and the estimators for subsystems communicate to exchange information. It is shown that when there is no fault, the estimation error of the distributed estimation system converges to zero in the absence of system disturbances and measurement noise. For each subsystem, a state predictor is also designed to provide subsystem state predictions. A residual generator is designed for each subsystem based on subsystem state estimates given by the distributed state estimation system and subsystem state predictions given by the predictor. A subsystem residual generator generates two residual sequences, which act as references for FDI. A distributed FDI mechanism is proposed based on residuals. The proposed approach is able to handle both actuator faults and sensor faults by evaluating the residual signals. A chemical process example is introduced to demonstrate the effectiveness of the distributed FDI mechanism. © 2017 American Institute of Chemical Engineers AIChE J, 63: 4329–4342, 2017
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".