ALMA engineering fault detection framework

José L. Ortiz, Rodrigo A. Carrasco

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


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.

Original languageEnglish
Title of host publicationObservatory Operations
Subtitle of host publicationStrategies, Processes, and Systems VII
EditorsRobert L. Seaman, Alison B. Peck, Chris R. Benn
ISBN (Electronic)9781510619616
StatePublished - 2018
Externally publishedYes
EventObservatory Operations: Strategies, Processes, and Systems VII 2018 - Austin, United States
Duration: 11 Jun 201815 Jun 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceObservatory Operations: Strategies, Processes, and Systems VII 2018
Country/TerritoryUnited States


  • Automation
  • Fault detection
  • Fault diagnosis
  • Framework
  • Predictive maintenance


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