Abstract—This paper proposes a maintenance platform for
business vehicles which detects failure sign using IoT data on
the move, orders to create repair parts by 3D printers and to
deliver them to the destination. Recently, IoT and 3D printer
technologies have been progressed and application cases to
manufacturing and maintenance have been increased.
Especially in air flight industry, various sensing data are
collected during flight by IoT technologies and parts are
created by 3D printers. And IoT platforms which improve
development/operation of IoT applications also have been
appeared. However, existing IoT platforms mainly targets to
visualize "things" statuses by batch processing of collected
sensing data, and 3 factors of real-time, automatic orders of
repair parts and parts stock cost are insufficient to accelerate
businesses. This paper targets maintenance of business vehicles
such as airplane or high-speed bus. We propose a maintenance
platform with real-time analysis, automatic orders of repair
parts and minimum stock cost of parts. The proposed platform
collects data via closed VPN, analyzes stream data and predicts
failures in real-time by online machine learning framework
Jubatus, coordinates ERP or SCM via in memory DB to order
repair parts and also distributes repair parts data to 3D
printers to create repair parts near the destination.
Index Terms—3D printer, predictive maintenance, local
production, industry 4.0, cloud computing, Jubatus, vehicle
maintenance.
Yoji Yamato and Hiroki Kumazaki are with the Software Innovation
Center, NTT Corporation, Tokyo, Japan (e-mail: yamato.yoji@lab.ntt.co.jp,
kumazaki.hiroki@lab.ntt.co.jp).
Yoshifumi Fukumoto was with the Software Innovation Center, NTT
Corporation, Tokyo, Japan (e-mail: fukumoto.yoshifumi@lab.ntt.co.jp).
[PDF]
Cite:Yoji Yamato, Yoshifumi Fukumoto, and Hiroki Kumazaki, "Proposal of Real Time Predictive Maintenance Platform with 3D Printer for Business Vehicles," International Journal of Information and Electronics Engineering vol. 6, no. 5, pp. 289-293, 2016.
