A Tradeoff Approach for Optimal Event-Triggered Fault Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, an optimal fault detection scheme is presented for a closed-loop control system, where an event-triggering mechanism is utilized for the transmission of output measurements, addressing the design of residual generation and evaluation, while considering the effects associated with unknown disturbances and faults in the system and event-triggered transmission errors on the generated residual. The triggering parameter-dependent residual generation is designed to achieve a best tradeoff between robustness against unknown disturbances and sensitivity to faults, and the residual evaluation is designed to deliver a time-varying threshold that accounts for the effects of disturbances and event-triggered transmission errors on the generated residual. The results are general and simplify to those developed for optimal fault detection in time-triggered systems. In addition, a vehicle lateral dynamic system is adopted to demonstrate the applicability of the proposed optimal fault detection scheme as well as its advantages over an existing widely used event-triggered fault detection scheme.
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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 | 0.000 |
| 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 it