A robust alternative to correlation networks for identifying fault systems
|Titel||A robust alternative to correlation networks for identifying fault systems|
|Buchtitel||Proceedings of the 26th International Workshop on Principles of Diagnosis (DX-15)|
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.