An adaptive SIF and KF estimation strategy for fault detection based on the NIS metric
Bibliographic record
Abstract
State estimation strategies play an essential role in the effective operation of dynamic systems by extracting relevant information about the system’s state when faced with limited measurement capability, sensor noise, or uncertain dynamics. The Kalman filter (KF) is one of the most commonly used filters and provides an optimal estimate for linear state estimation problems. However, the KF lacks robustness as it does not perform well in the face of modelling uncertainties and disturbances. The sliding innovation filter (SIF) is a newly proposed filter that uses a switching gain and innovation term, and unlike the KF, it only results in a sub-optimal estimate. However, the SIF has been proven to be robust to modelling uncertainties, disturbances, and ill-conditioned problems. In this work, we propose an adaptive SIF and KF (SIF-KF) estimation algorithm that can detect faulty or uncertain conditions and switch between the KF and SIF gain in the absence or presence of such conditions, respectively. A fault detection mechanism based on the normalized innovation squares (NIS) metric is also presented, which is responsible for triggering the activation of the respective gain in the proposed SIF-KF strategy. Experimental simulations are carried out on a simple harmonic oscillator subject to a fault to demonstrate the proposed SIF-KF’s effectiveness over traditional approaches.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".