Closed‐loop design of fault detection for networked non‐linear systems with mixed delays and packet losses
Bibliographic record
Abstract
This study is concerned with the problem of fault detection (FD) for networked control systems with discrete and infinite distributed delays subject to random packet losses and non‐linear perturbation. Both sensor‐to‐controller and controller‐to‐actuator packet losses are modelled as two different mutually independent Bernoulli distributed white sequences with known conditional probability distributions. By utilising an observer‐based fault detection filter (FDF) as a residual generator, the FD for networked non‐linear systems with mixed delays and packet losses is formulated as an H ∞ model‐matching problem. Attention is focused on designing the FDF in the closed‐loop system setup such that the estimation error between the residuals and filtered faults is made as small as possible and at the same time the closed‐loop networked non‐linear system is exponentially stable in the mean‐square sense. To show the superiority and effectiveness of this work, two numerical examples are presented.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".