The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
TY - CONF AU - Fol, E. AU - Coello de Portugal, J.M. AU - Tomás, R. ED - Boland, Mark ED - Tanaka, Hitoshi ED - Button, David ED - Dowd, Rohan ED - Schaa, Volker RW ED - Tan, Eugene TI - Unsupervised Machine Learning for Detection of Faulty Beam Position Monitors J2 - Proc. of IPAC2019, Melbourne, Australia, 19-24 May 2019 CY - Melbourne, Australia T2 - International Particle Accelerator Conference T3 - 10 LA - english AB - Unsupervised learning includes anomaly detection techniques that are suitable for the detection of unusual events such as instrumentation faults in particle accelerators. In this work we present the application of decision trees-based algorithm to faulty BPMs detection at the LHC. This method achieves significant improvements in quality of optics measurements and allows to identify relevant signal properties that contribute to fault detection. PB - JACoW Publishing CP - Geneva, Switzerland SP - 2668 EP - 2671 DA - 2019/06 PY - 2019 SN - 978-3-95450-208-0 DO - DOI: 10.18429/JACoW-IPAC2019-WEPGW081 UR - http://jacow.org/ipac2019/papers/wepgw081.pdf ER -