期刊
OBSERVATORY OPERATIONS: STRATEGIES, PROCESSES, AND SYSTEMS VII
卷 10704, 期 -, 页码 -出版社
SPIE-INT SOC OPTICAL ENGINEERING
DOI: 10.1117/12.2312285
关键词
fault detection; fault diagnosis; framework; automation; predictive maintenance
资金
- ESO
- NSF
- National Research Council of Canada (NRC)
- National Science Council of Taiwan (NSC)
- Academia Sinica (AS) in Taiwan
- Korea Astronomy and Space Science Institute (KASI)
- NINS
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.
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