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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 opera...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2023
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| _version_ | 1867613152579944448 |
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| access_status_str | Open Access |
| author | Roelf, Timothy Brian |
| author2 | Groot, Paul Joseph |
| author_browse | Groot, Paul Joseph Roelf, Timothy Brian |
| author_facet | Groot, Paul Joseph Roelf, Timothy Brian |
| author_sort | Roelf, Timothy Brian |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/37797 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:35.974Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/37797 Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope Roelf, Timothy Brian Groot, Paul Joseph Rakotonirainy, Rosephine Georgina Data Science 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. 2023-04-20T11:27:44Z 2023-04-20T11:27:44Z 2022 2023-04-20T09:14:44Z Master Thesis Masters MSc http://hdl.handle.net/11427/37797 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Data Science Roelf, Timothy Brian Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope |
| thesis_degree_str | Master's |
| title | Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope |
| title_full | Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope |
| title_fullStr | Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope |
| title_full_unstemmed | Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope |
| title_short | Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope |
| title_sort | log mining to develop a diagnostic and prognostic framework for the meerlicht telescope |
| topic | Data Science |
| url | http://hdl.handle.net/11427/37797 |
| work_keys_str_mv | AT roelftimothybrian logminingtodevelopadiagnosticandprognosticframeworkforthemeerlichttelescope |