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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 26th International Workshop on Principles of Diagnosis (DX-15) |
| Pages | 11-18 |
| Number of pages | 8 |
| Publication status | Published - Aug 2015 |
Fields of science
- 202007 Computer integrated manufacturing (CIM)
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102006 Computer supported cooperative work (CSCW)
- 102010 Database systems
- 102014 Information design
- 102015 Information systems
- 102016 IT security
- 102022 Software development
- 102025 Distributed systems
- 502007 E-commerce
- 505002 Data protection
- 506002 E-government
- 509018 Knowledge management
JKU Focus areas
- Computation in Informatics and Mathematics
