Kalman filter for parametric fault detection: an internal model principle-based approach
Bibliographic record
Abstract
The paramount importance of fault detection (FD) in complex engineering systems has undoubtedly been the main driver behind the development of a plethora of techniques in the FD area. In this study, the authors propose an internal model principle-based Kalman filter (IMP-KF) structure for use in the detection of parametric faults. The authors show that the closed-loop structure of the IMP-KF is indeed a necessary and sufficient condition for generating residuals upon which the FD process hinges. They advocate a residual generator structure similar to that used in the standard Kalman filtering (KF), and judiciously exploit the non-robustness to model mismatch of the proposed IMP-KF scheme to detect faults in the presence of noise and disturbances. With no model mismatch, the KF residual’s whiteness is exploited to derive a composite hypothesis testing that accounts for a low probability for false alarm and a high probability of correct decision for various reference inputs. The proposed scheme was successfully evaluated on both simulated and physical systems.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".