Unbiased inversion‐based fault estimation of systems with non‐minimum phase fault‐to‐output dynamics
Bibliographic record
Abstract
In this study, the authors propose a framework for inversion‐based estimation of certain categories of faults in discrete‐time linear systems. The fault signal, as an unknown input, is reconstructed from its projections onto two subspaces. The first projection is achieved through an algebraic operation, whereas the second projection is obtained by a dynamic filter whose poles coincide with the transmission zeros of the system. Feedback is then introduced to stabilise this filter as well as to provide an unbiased estimate of the unknown input. Their proposed methodology has two distinctive and practical advantages. First, it represents a unified approach to the problem of inversion of both minimum and non‐minimum phase systems as well as systems having transmission zeros on the unit circle. Second, the feedback structure ensures that the proposed scheme is robust to noise. They have shown that the proposed inversion filter is unbiased for certain categories of faults. Finally, they have demonstrated the capabilities and performance of the proposed methodology through several numerical simulation case studies.
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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 | 0.000 | 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".