20.01.2023 Articles
Caverion Intelligence recognised as a good practice for nuclear power plants by IAEA
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Caverion Intelligencerecognised as a good practice for nuclear power plants by IAEA

The International Atomic Energy Agency (IAEA) completed a review of long-term operational safety at the Oskarshamn Nuclear Power Plant Unit 3 in Sweden last autumn. One of the good practices IAEA's expert team identified at the plant, is the machine learning software used to monitor turbine performance, known as Caverion Intelligence.

"Thanks to Caverion Intelligence, we have been able to identify and fix several anomalies before they become actual technical faults," says Sune Jonsson, Senior Advisor Engineering Department, OKG AB.

Caverion Intelligenceutilises machine learning and process data to monitor the operation of the turbine and detects anomalies before they develop into operational issues. To meet the extremely strict safety standards at the plant, Caverion Intelligence is highly secured, yet user-friendly. The service includes regular follow-up meetings with the Caverion experts.

"IAEA will share their findings with the nuclear industry globally, which is a great recognition to our innovation expertise. Caverion Intelligence is based on a highly advanced machine learning solution and fits to a large number of applications. At the moment, it is helping customers for example in energy, chemical and metal industries," says Harri Paukkeri, Head of Caverion Intelligence.

Read more:

  • Caverion Intelligence AI solution ensures operational reliability of OKG's nuclear power plant
  • Orion: technical maintenance to support high reliability and advanced machine learning to detect early signs of abnormal situations in the process
  • SSAB: Caverion Intelligence in use on galvanising line in Hämeenlinna, Finland
  • Data-driven production at Stockholm Exergi with Caverion Intelligence
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Caverion Oyj published this content on 20 January 2023 and is solely responsible for the information contained therein. Distributed by Public, unedited and unaltered, on 20 January 2023 13:10:02 UTC.