ALMA engineering fault detection framework

Abstract

The Atacama Large Millimeter/Submillimeter Array (ALMA) Observatory, with its 66 individual radiotelescopes and other central equipment, generates a massive set of monitoring data everyday, collecting information on the performance of a variety of critical and complex electrical, electronic, and mechanical components. By using this crucial data, engineering teams have developed and implemented both model and machine learning-based fault detection methodologies that have greatly enhanced early detection or prediction of hardware malfunctions. This paper presents the results of the development of a fault detection and diagnosis framework and the impact it has had on corrective and predictive maintenance schemes.

Publication
Observatory Operations: Strategies, Processes, and Systems VII
José Luis Ortiz
José Luis Ortiz
ALMA Observatory
Rodrigo A. Carrasco
Rodrigo A. Carrasco
Academic Director Master in Industrial Engineering / Assistant Professor

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