<title>Real-time system diagnosis with sensors of uncertain quality</title>
Bibliographic record
Abstract
This paper presents a real time approach to the detection and isolation of component failures in largescale systems. The algorithm is given a set of observed test results from multiple sensors, and its main task is to deal with sensor errors (i.e., noise). The probabilities of these missed detections and false alarms are not known a-priori, and must be estimated - ideally along with the accuracies of these estimates - online, within the inference engine. Further, recognizing a practical concern in most real systems, a sparsely instantiated observation vector must not be a problem. The key ingredients to the approach include the Multiple Hypothesis Tracking (MHT) philosophy to complexity management, and a Beta prior distribution on the sensor errors. We provide results illustrating performance in terms of both computational needs and error rate, and show its application both as a filter (i.e., used to "clean" sensor reports) and as a standalone state estimator.
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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".