Fault Detection in Nonlinear Stable Systems Over Lossy Networks
Bibliographic record
Abstract
This paper addresses the problem of fault detection (FD) in nonlinear stable systems, which are monitored via communications networks. An FD based on the system data provided by the communications network is called networked fault detection (NFD) or over network FD in the literature. A residual signal is generated, which gives a satisfactory estimation of the fault. A sufficient condition is derived, which minimizes the estimation error in the presence of packet drops, quantization error, and unwanted exogenous inputs such as disturbance and noise. A linear matrix inequality is obtained for the design of the FD filter parameters. In order to produce appropriate fault alarms, two widely used residual signal evaluation methodologies, based on the signals' peak and average values, are presented and compared together. Finally, the effectiveness of the proposed NFD technique is extensively assessed by using an experimental testbed that was built for performance evaluation of such systems with the use of IEEE 802.15.4 wireless sensor networks (WSNs) technology. In particular, this paper describes the issue of floating point calculus when connecting the WSNs to the engineering design softwares, such as MATLAB, and a possible solution is 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".