Abstract
We study the situation in which many systems relate to each other. We show how to robustly learn relations between systems to conduct fault detection and identification (FDI), i.e. the goal is to identify the faulty systems. Towards this, we present a robust alternative to the sample correlation matrix and show how to randomly search in it for a structure appropriate for FDI. Our method applies to situations in which many systems can be faulty simultaneously and thus our method requires an appropriate degree of redundancy. We present experimental results with data arising in photovoltaics and supporting theoretical results.
| Originalsprache | Englisch |
|---|---|
| Titel | Proceedings of the 26th International Workshop on Principles of Diagnosis (DX-15) |
| Seiten | 11-18 |
| Seitenumfang | 8 |
| Publikationsstatus | Veröffentlicht - Aug. 2015 |
UN SDGs
Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung
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SDG 9 – Industrie, Innovation und Infrastruktur -
SDG 16 – Frieden, Gerechtigkeit und starke Institutionen
Wissenschaftszweige
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JKU-Schwerpunkte
- Computation in Informatics and Mathematics
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