{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/37797"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/37797","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Roelf, Timothy Brian"],"institution":"Department of Statistical Sciences","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Groot, Paul Joseph","Rakotonirainy, Rosephine Georgina"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022","date_published":"2022","updated_at":"2026-07-24T01:34:13Z","subjects":["Data Science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/37797","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Groot, Paul Joseph","Rakotonirainy, Rosephine Georgina"]},{"key":"dc:creator","label":"Author","values":["Roelf, Timothy Brian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-04-20T11:27:44Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-04-20T11:27:44Z"]},{"key":"dc:date.issued","label":"Date","values":["2022"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Statistical Sciences"]},{"key":"dc:type","label":"Dc Type","values":["Master Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters","MSc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Data Science"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/37797"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Log mining to develop a diagnostic and prognostic framework for the MeerLICHT telescope"]}]}],"canonical_facts":{"dc:contributor.advisor":["Groot, Paul Joseph","Rakotonirainy, Rosephine Georgina"],"dc:creator":["Roelf, Timothy Brian"],"dc:date.accessioned":["2023-04-20T11:27:44Z"],"dc:date.available":["2023-04-20T11:27:44Z"],"dc:date.issued":["2022"],"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.uri":["http://hdl.handle.net/11427/37797"],"dc:publisher.department":["Department of Statistical Sciences"],"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","MSc"]},"updated_at":"2026-07-24T01:34:13Z"}