Clustering of Redundant Parameters for Fault Isolation with Gaussian Residuals
Bibliographic record
Abstract
Fault detection and isolation in stochastic systems is typically model-based, meaning fault-indicating residuals are generated based on measurements and compared to equivalent mathematical system models. The residuals often exhibit Gaussian properties or can be transformed into a standard Gaussian framework by means of the asymptotic local approach. The effectiveness of the fault diagnosis depends on the model quality, but an increasing number of model parameters also leads to redundancies which, in turn, can distort the fault isolation. This occurs, for example, in structural engineering, where residuals are generated by comparing structural vibrations to the output of digital twins. This article proposes a framework to find the optimal parameter clusters for such problems. It explains how the optimal solution is a compromise, because with an increasing number of clusters, the fault isolation resolution increases, but the detectability in each cluster decreases, and the number of false alarms changes. To assess these factors during the clustering process, criteria for the minimum detectable change and the false-alarm susceptibility are introduced and evaluated in an optimization scheme.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".