An Alternative Approach to Implementation of the Generalized Likelihood Ratio Test for Fault Detection and Isolation
Bibliographic record
Abstract
In this study an alternative approach to implementation of the generalized likelihood ratio (GLR) test for detection and isolation of the fault in linear systems is proposed. The proposed approach offers the following advantages: 1) It overcomes the shortcomings of the previously suggested methods by accurately detecting the time of occurrence of the fault; 2) It uses statistical fault detection and confirmation tests to obtain a crude estimate of time of occurrence of the fault (TOF) and then refines the estimated TOF using an extended data window and the GLR test; and 3) It avoids performing the isolation in case the number of data points is not enough and hence the number of misclassifications is significantly reduced. The newly proposed method is evaluated by application to a benchmark CSTR problem using Monte Carlo simulations, and the results reveal that this method can estimate the time of occurrence of the fault and the fault magnitude more accurately compared to a previous approach applied to the same benchmark problem. Simulation results on a benchmark problem also show significantly lower misclassification rates.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".