Year: 2025
Type: article
Abstract: Predicting the condition of machinery and performing maintenance activities in advance is essential for efficient and safe work at construction sites. If the load applied to a machine in operation is known, it is possible to predict failures based on the load conditions. However, the load on construction machinery such as hydraulic excavators changes depending on the work environment and operation, so it is necessary to construct a load estimation model that adapts to these factors. This paper proposes a load estimation modeling method that combines machine learning to determine excavator movements and database-driven modeling. Experiments using a radio-controlled excavator show that the proposed method is effective.
DOI: 10.5687/iscie.38.119
Authors: Kazuki Naruta, Toru Yamamoto, Shota Oguma, Kazuhiro Iwasaki, Kenta Kojima
Institutions: Hiroshima University, Kobelco Construction Machinery (Japan)
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7081969030