Observer-based Fault Detection of Technical Systems over Networks
Bibliographic record
Abstract
Behind every doctoral thesis lie years of hard work. It is therefore a great pleasure for me to thank those now who have given me the support and encouragement during all this time. This thesis was written while the author was with the Institute for Automatic Control and Complex Systems (AKS) in the Faculty of Engineering at the University of Duisburg-Essen in Germany. I would like to thank Prof. Dr.-Ing. Steven X. Ding, the head of the institute, for his assistance in preparation of this thesis. His permanent support and interest enabled the presentation of this work. I would also like to thank Prof. PhD Qing Zhao from the University of Alberta for being my second supervisor. I would like to express my gratitude to Prof. Dr.-Ing. Andreas Czylwik and M.Sc. Oliver Bredtmann from the Department of Communication Systems at the University of Duisburg-Essen for the cooperation and discussion in our research project. Many thanks to all colleagues from the institute for making an inspiring and pleasant atmosphere. Special thanks to Dr.-Ing Ping Zhang, M.Sc. Cristian I. Chihaia, Dipl.-Ing Eberhard Goldschmidt, Dr.-Ing Ibrahim Al-Salami for valuable discussions and helpful
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".