Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope

In this work we present the approach taken to address the problems anomalous fault detection and system delays experienced by the MeerLICHT telescope. We make use of the abundantly available console logs, that record all aspects of the telescope's function, to obtain information. The MeerLICHT operational team must devote time to manually inspecting the logs during system downtime to discover faults. This task is laborious, time inefficient given the large size of the logs, and does not suit the time-sensitive nature of many of the surveys the telescope partakes in. We used the novel approach of the Hidden Markov model, to address the problems of fault detection and system delays experienced by the MeerLICHT. We were able to train the model in three separate ways, showing some success at fault detection and none at the addressing the system delays.

Grantor

Department of Statistical Sciences

Year dc:date.issued

2022

Author dc:creator

• Roelf, Timothy Brian

Advisors dc:contributor.advisor

• Groot, Paul Joseph
• Rakotonirainy, Rosephine Georgina

• Data Science

Handle dc:identifier.uri

http://hdl.handle.net/11427/37797

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/37797