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@InProceedings{fernandezcarmona:icalepcs2019-thcpl06,
author = {P. Fernandez Carmona and Z. Chowdhuri and S.G. Ebner and F. Gagnon-Moisan and M. Grossmann and J. Snuverink and D.C. Weber},
% author = {P. Fernandez Carmona and Z. Chowdhuri and S.G. Ebner and F. Gagnon-Moisan and M. Grossmann and J. Snuverink and others},
% author = {P. Fernandez Carmona and others},
title = {{Introducing Big Data Analysis in a Proton Therapy Facility to Reduce Technical Downtime}},
booktitle = {Proc. ICALEPCS'19},
pages = {1608--1611},
paper = {THCPL06},
language = {english},
keywords = {proton, controls, interlocks, status, EPICS},
venue = {New York, NY, USA},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {17},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2020},
issn = {2226-0358},
isbn = {978-3-95450-209-7},
doi = {10.18429/JACoW-ICALEPCS2019-THCPL06},
url = {https://jacow.org/icalepcs2019/papers/thcpl06.pdf},
note = {https://doi.org/10.18429/JACoW-ICALEPCS2019-THCPL06},
abstract = {At the center for Proton Therapy of the Paul Scherrer Institute about 450 cancer patients are treated yearly using accelerated protons in three treatment areas. The facility is active since 1984 and for each patient we keep detailed log files containing machine measurements during each fraction of the treatment, which we analyze daily to guarantee dose and position values within the prescribed tolerances. Furthermore, each control and safety system generates textual log files as well as periodic measurements such as pressure, temperature, beam intensity, magnetic fields or reaction time of components. This adds up currently to approximately 5 GB per day. Downtime of the facility is both inconvenient for patients and staff, as well as financially relevant. This article describes how we have extended our data analysis strategies using machine archived parameters and online measurements to understand interdependencies, to perform preventive maintenance of ageing components and to optimize processes. We have chosen Python to interface, structure and analyze the different data sources in an standardized manner. The online channels have been accessed via an EPICS archiver.},
}