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RIS citation export for TUPHA030: Using AI in the Fault Management Predictive Model of the SKA TM Services: A Preliminary Study

TY - CONF
AU - Canzari, M.
AU - Di Carlo, M.
AU - Dolci, M.
AU - Smareglia, R.
ED - Schaa, Volker RW
ED - Costa, Isidre
ED - Fernández, David
ED - Matilla, Óscar
TI - Using AI in the Fault Management Predictive Model of the SKA TM Services: A Preliminary Study
J2 - Proc. of ICALEPCS2017, Barcelona, Spain, 8-13 October 2017
C1 - Barcelona, Spain
T2 - International Conference on Accelerator and Large Experimental Control Systems
T3 - 16
LA - english
AB - SKA (Square Kilometer Array) is a project aimed to build a very large radio-telescope, composed by thousands of antennae and related support systems. The overall orchestration is performed by the Telescope Manager (TM), a suite of software applications. In order to ensure the proper and uninterrupted operation of TM, a local monitoring and control system is developed, called TM Services. Fault Management (FM) is one of these services, and is composed by processes and infrastructure associated with detecting, diagnosing and fixing faults, and finally returning to normal operations. The aim of the study, introducing artificial intelligence algorithms during the detection phase, is to build a predictive model, based on the history and statistics of the system, in order to perform trend analysis and failure prediction. Based on monitoring data and health status detected by the software system monitor and on log files gathered by the ELK (Elasticsearch, Logstash, and Kibana) server, the predictive model ensures that the system is operating within its normal operating parameters and takes corrective actions in case of failure.
PB - JACoW
CP - Geneva, Switzerland
SP - 435
EP - 438
KW - ion
KW - software
KW - monitoring
KW - ISOL
KW - operation
DA - 2018/01
PY - 2018
SN - 978-3-95450-193-9
DO - 10.18429/JACoW-ICALEPCS2017-TUPHA030
UR - http://jacow.org/icalepcs2017/papers/tupha030.pdf
ER -