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Failure Prediction Based on Operational Data of Hydraulic Excavator with Machine Learning
Author(s) -
Oguma Shota,
Omatsu Shigeru,
Ohno Shuichi,
Iwasaki Kazuhiro,
Shishido Yoshiaki
Publication year - 2021
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.23443
Subject(s) - excavator , realization (probability) , space (punctuation) , computer science , big data , artificial intelligence , machine learning , engineering , industrial engineering , mechanical engineering , data mining , mathematics , operating system , statistics
In ‘Society5.0’, realization of super smart society will be possible by analyzing big data in the cyber space (virtual space) and by feeding back useful information to the physical space (real space). In the construction industry, since unexpected machine failures are huge losses for users who have to proceed with construction according to their construction plans, machine breakdowns must be avoided. In this letter, we predict failures of lower traveling bodies of hydraulic excavators using machine learning methods. Numerical examples are provided to show the effectiveness of the proposed methods. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.