Predictive maintenance and safety operation by device integration on the QUEST large experimental device

Makoto Hasegawa, Kazuaki Hanada, Hiroshi Idei, Shoji Kawasaki, Takahiro Nagata, Ryuya Ikezoe, Takumi Onchi, Kengoh Kuroda, Aki Higashijima

Research output: Contribution to journalArticlepeer-review

Abstract

As technology has improved in recent years, it has become possible to create new valuable functions by combining various devices and sensors in a network. This concept is referred to as the Internet of Things (IoT), and predictive maintenance is a new valuable function associated with the IoT. In large-scale experimental facilities with many researchers, it is not desirable that experiments cannot be performed due to sudden failure of equipment. For this reason, it is important to predict the failure in advance based on the measurement results of sensors and to perform repairs in a planned manner. On the Q-shu University experiment with steady-state spherical tokamak (QUEST) large experimental device, it is necessary to drive a large current of 50 kA, and the diagnosis of its power line deterioration is well performed as predictive maintenance through the evaluation of its contact resistances of several micro ohms order on the network. In addition, as an example of the IoT, mechanisms to assist safe operation, such as a sound alarm system and an entrance management system, are built by sharing experimental information between devices via the network.

Original languageEnglish
Article numbere04214
JournalHeliyon
Volume6
Issue number6
DOIs
Publication statusPublished - Jun 2020

All Science Journal Classification (ASJC) codes

  • General

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