Failure Prediction based on Operational Data of Hydraulic Excavator with Machine Learning

Failure Prediction based on Operational Data of Hydraulic Excavator with Machine Learning
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基于机器学习的液压挖掘机运行数据的故障预测

DOI：
10.1002/tee.23443
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发表时间：
2021
期刊：
IEEJ Trans. on Electrical and Electronic Engineering
影响因子：
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通讯作者：
Shota Oguma，Shigeru Omatsu，Shuichi Ohno，Kazuhiro Iwasaki，Yoshiaki Shishido
Shota Oguma，Shigeru Omatsu，Shuichi Ohno，Kazuhiro Iwasaki，Yoshiaki Shishido
中科院分区：
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文献类型：
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作者：
Yuta Kanda;Kota S Sasaki;Izumi Ohzawa;Hiroshi Tamura;Shota Oguma，Shigeru Omatsu，Shuichi Ohno，Kazuhiro Iwasaki，Yoshiaki Shishido

文献摘要

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在“Society5.0”中，通过分析网络空间（虚拟空间）中的大数据，并将有用的信息反馈到物理空间（真实的空间），实现超级智慧社会成为可能。在建筑行业中，由于意外的机器故障对用户来说是巨大的损失，用户必须按照他们的施工计划进行施工，因此必须避免机器故障。在本文中，我们使用机器学习方法来预测液压挖掘机下部行走体的故障。最后利用数值仿真验证所提出方法的有效性.© 2021日本电机工程师学会。由Wiley期刊有限责任公司出版。
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.