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BiBTeX citation export for THPV039: Machine learning applications for accelerator failure prevention at MAX IV

  author       = {J.E. Petersson and B. Meirose},
  title        = {{Machine learning applications for accelerator failure prevention at MAX IV}},
  booktitle    = {Proc. ICALEPCS'21},
  language     = {english},
  intype       = {presented at the},
  series       = {International Conference on Accelerator and Large Experimental Physics Control Systems},
  number       = {18},
  venue        = {Shanghai, China},
  publisher    = {JACoW Publishing, Geneva, Switzerland},
  month        = {03},
  year         = {2022},
  note         = {presented at ICALEPCS'21 in Shanghai, China, unpublished},
  abstract     = {{Machine learning (ML) applications have received renewed interest in recent years. The reason behind this lies in advances in ML methods, data availability and increased computational power. Application of ML techniques to diagnose or even prevent accelerator failures is an area of particular interest not least because of the ample data that is routinely gathered in all modern accelerators to conduct reliability studies. In this contribution we present preliminary results of the application of unsupervised learning to diagnose and decrease accelerator failure rates at MAX IV, focusing on systems and methods that presented the best results.}},