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Proceedings Paper

ALMA engineering fault detection framework
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Paper 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.

Paper Details

Date Published: 10 July 2018
PDF: 13 pages
Proc. SPIE 10704, Observatory Operations: Strategies, Processes, and Systems VII, 107042K (10 July 2018); doi: 10.1117/12.2312285
Show Author Affiliations
José L. Ortiz, ALMA (Chile)
Rodrigo A. Carrasco, Univ. Adolfo Ibáñez (Chile)

Published in SPIE Proceedings Vol. 10704:
Observatory Operations: Strategies, Processes, and Systems VII
Alison B. Peck; Robert L. Seaman; Chris R. Benn, Editor(s)

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