ALMA engineering fault detection framework

José L. Ortiz; Rodrigo A. Carrasco

doi:10.1117/12.2312285

10 July 2018 ALMA engineering fault detection framework

José L. Ortiz, Rodrigo A. Carrasco

Proceedings Volume 10704, Observatory Operations: Strategies, Processes, and Systems VII; 107042K (2018) https://doi.org/10.1117/12.2312285
Event: SPIE Astronomical Telescopes + Instrumentation, 2018, Austin, Texas, United States

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.

Citation Download Citation

José L. Ortiz and Rodrigo A. Carrasco "ALMA engineering fault detection framework", Proc. SPIE 10704, Observatory Operations: Strategies, Processes, and Systems VII, 107042K (10 July 2018); https://doi.org/10.1117/12.2312285

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