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

Research output: Contribution to journalArticlepeer-review

4 Citations (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.

Original languageEnglish
Article number113199
JournalFusion Engineering and Design
Publication statusPublished - Jul 2022

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Nuclear Energy and Engineering
  • General Materials Science
  • Mechanical Engineering


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