Fault detection using marginalized likelihood ratio and uniform priors: Justifications and challenges
Bibliographic record
Abstract
The marginalized likelihood ratio (MLR) approach to fault detection as proposed by Gustafsson [2] is based on the assumption of improper flat priors with infinite support for fault magnitude. This assumption leads to the problem that the likelihood function cannot be uniquely defined after the occurrence of the fault. In another approach by Dos Santos and Yoneyama [9] the prior is assumed to follow a Gamma distribution which is hard to justify as this selection of prior penalizes low and high magnitude faults. However, with presence of safety shutdown systems as well as range constraints on sensors and actuators, all process variables are generally bounded and this motivates one to investigate the possibility of using uniform priors for fault magnitudes. This study aims to undertake this task and attempts to discuss the justification and the challenges associated with selection of such priors. The outcome of this analysis is a new fault detection and isolation (FDI) scheme that takes advantage of the modified MLR (MMLR) test to accurately estimate the time of the occurrence of the fault. The proposed FDI uses the MMLR as the detector of time of occurrence of the fault and the generalized likelihood ratio (GLR) test for the purpose of isolation of the fault and estimation of its magnitude.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".