Fault Detection and Diagnosis of Multiple-Model Systems With Mismodeled Transition Probabilities
Bibliographic record
Abstract
This paper proposes an improved interacting multiple-model (I2MM) algorithm with inaccurate transition probabilities (TPs) for fault detection and diagnosis (FDD). We first study the influence of inaccurate TPs by inspecting the expectation and covariances of residual error vectors in the traditional IMM method. It shows that Kalman filters can be retained as subfilters in the presence of mismodeled TPs, and the effect of TPs can be removed naturally if all of the probabilities of true modes are equal to one. In view of this, a modification operator is proposed to make the real mode probabilities heuristically approach one. The modification degree is governed by a parameter determined by the online measurements. When the modification parameter calculated is identical to one, the I2MM method reduces to the conventional IMM algorithm. An experiment designed through a ball-and-tube testbed is presented to demonstrate that the I2MM-based FDD method can provide more reliable FDD results and reduce the possibility of false alarms.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".