Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope
| dc.contributor.advisor | Groot, Paul Joseph | |
| dc.contributor.advisor | Rakotonirainy, Rosephine Georgina | |
| dc.contributor.author | Roelf, Timothy Brian | |
| dc.date.accessioned | 2023-04-20T11:27:44Z | |
| dc.date.available | 2023-04-20T11:27:44Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2023-04-20T09:14:44Z | |
| dc.description.abstract | 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. | |
| dc.identifier.apacitation | Roelf, T. B. (2022). <i>Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope</i>. (). ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/37797 | en_ZA |
| dc.identifier.chicagocitation | Roelf, Timothy Brian. <i>"Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope."</i> ., ,Faculty of Science ,Department of Statistical Sciences, 2022. http://hdl.handle.net/11427/37797 | en_ZA |
| dc.identifier.citation | Roelf, T.B. 2022. Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope. . ,Faculty of Science ,Department of Statistical Sciences. http://hdl.handle.net/11427/37797 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Roelf, Timothy Brian AB - 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. DA - 2022_ DB - OpenUCT DP - University of Cape Town KW - Data Science LK - https://open.uct.ac.za PY - 2022 T1 - Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope TI - Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope UR - http://hdl.handle.net/11427/37797 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/37797 | |
| dc.identifier.vancouvercitation | Roelf TB. Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope. []. ,Faculty of Science ,Department of Statistical Sciences, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/37797 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Statistical Sciences | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | Data Science | |
| dc.title | Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |