Towards automated gas leak detection through cluster analysis of mass spectrometer data

Makoto Hasegawa, Daisuke Sakurai, Aki Higashijima, Ichiro Niiya, Keiji Matsushima, Kazuaki Hanada, Hiroshi Idei, Takeshi Ido, Ryuya Ikezoe, Takumi Onchi, Kengo Kuroda

研究成果: ジャーナルへの寄稿学術誌査読

4 被引用数 (Scopus)


In order to generate high-performance plasma for future fusion power generation, it is desirable to keep high quality vacuum during experiment. Mass spectrometer is commonly used to monitor the vacuum quality and to record the amount of atoms and molecules in the vacuum vessel. Leak is the most serious accident to avoid that can nullify an experiment and even harm researchers. Detecting leaks are ever more important since it can be easily overlooked, e.g., when the deterioration in the vacuum degree is modest. This forces the researcher to carefully observe the vacuum and mass spectrometer data. This article presents a way to suggest potential leaks in the vacuum vessel by analyzing mass spectrometer data. This is done by utilizing the Euclidean distance between composition ratios at different times for the clustering using the daily composition ratio. We show that our cluster analysis is an effective way of separating these two cases, which results in a semi-automatic determination of leaks is more efficient than the current norm, which is to check many measures to find a small abnormality in the data manually. We plan further model improvements for long-term evaluation.

ジャーナルFusion Engineering and Design
出版ステータス出版済み - 7月 2022

!!!All Science Journal Classification (ASJC) codes

  • 土木構造工学
  • 原子力エネルギーおよび原子力工学
  • 材料科学一般
  • 機械工学


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