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TY - CONF AU - Martensson, K.O.E. AU - Bradu, B. AU - Ferlin, G. ED - White, Karen S. ED - Brown, Kevin A. ED - Dyer, Philip S. ED - Schaa, Volker RW TI - Signal Analysis for Automated Diagnostic Applied to LHC Cryogenics J2 - Proc. of ICALEPCS2019, New York, NY, USA, 05-11 October 2019 CY - New York, NY, USA T2 - International Conference on Accelerator and Large Experimental Physics Control Systems T3 - 17 LA - english AB - The operation of the LHC at CERN is highly dependent on its associated infrastructure to operate properly, such as its cryogenic system where many conditions must be fulfilled for superconducting magnets and RF cavities. In 2018, the LHC cryogenic system caused 172 hours of accelerator downtime (out of 5760 running hours). Since the cryogenics recovery acts as a time amplifier, it is important to identify not optimized processes and malfunctioning systems at an early stage to anticipate losses of availability. The LHC cryogenic control systems embeds about 60,000 I/O whereof more than 20,000 analog signals which have to be monitored by operators. It is therefore crucial to select only the relevant and necessary information to be presented. This paper presents a signal analysis system created to automatically generate adequate daily reports on potential problems in the LHC cryogenic system which are not covered by conventional alarms, and examples of real issues that have been found and treated during the 2018 physics run. The analysis system, which is written in Python, is generic and can be applied to many different systems. PB - JACoW Publishing CP - Geneva, Switzerland SP - 1601 EP - 1607 KW - cryogenics KW - software KW - Windows KW - vacuum KW - controls DA - 2020/08 PY - 2020 SN - 2226-0358 SN - 978-3-95450-209-7 DO - doi:10.18429/JACoW-ICALEPCS2019-THCPL05 UR - https://jacow.org/icalepcs2019/papers/thcpl05.pdf ER -