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@InProceedings{dottavio:icalepcs2019-thcpl07,
author = {Ottavio D’ and T. D'Ottavio and P.S. Dyer and J. Piacentino and M.R. Tomko},
title = {{Experience Using NuPIC to Detect Anomalies in Controls Data}},
booktitle = {Proc. ICALEPCS'19},
pages = {1612--1616},
paper = {THCPL07},
language = {english},
keywords = {software, real-time, controls, GUI, framework},
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-THCPL07},
url = {https://jacow.org/icalepcs2019/papers/thcpl07.pdf},
note = {https://doi.org/10.18429/JACoW-ICALEPCS2019-THCPL07},
abstract = {NuPIC (Numenta Platform for Intelligent Computing) is an open-source computing platform that attempts to mimic neurological pathways in the human brain. We have used the Python implementation to explore the utility of using this system to detect anomalies in both stored and real-time data coming from the controls system for the RHIC Collider at Brookhaven National Laboratory. This paper explores various aspects of that work including the types of data most suited to anomaly detection, the likelihood of developing false positive and negative anomaly results, and experiences with training the system. We also report on the use of this software for monitoring various parts of the controls system in real-time.},
}