講演詳細

利用部門[Forest Engineering]

日付	2025年3月22日
開始時刻	11:00
会場名	N21
講演番号	K-2
発表題目	Automated Long-Term Productivity Analysis of Feller Bunchers in Whole-Tree Harvesting in British Columbia, Canada Automated Long-Term Productivity Analysis of Feller Bunchers in Whole-Tree Harvesting in British Columbia, Canada
所属	Utsunomiya University
要旨本文	Production and utilization data are essential for informed decision making and productivity analysis in logging. Whole-tree (WT) harvesting machines, however, have historically lacked the comprehensive, automated data collection found in cut-to-length (CTL) operations.This study introduces a scalable approach for automated data collection and productivity analysis in WT harvesting. Building on earlier proof-of-concept work, the approach uses engine status data and GNSS points from FPDat II onboard computers, integrated with volume distribution information, to create daily production reports. Implemented across 500 machines in British Columbia, this system allows near real-time operational monitoring at block- and shift-levels.By applying outlier criteria and leveraging long-term machine production data, robust productivity models were developed. This framework supports advanced machine learning for productivity predictions and drives digital transformation in forest operations.
著者氏名	○Steffen T. Lahrsen1,2 ・ Omar Mologni1 ・ Dominik Röser1
著者所属	1Forest Resource Management, Faculty of Forestry, University of British Columbia ・ 2Department of Forest Science, Utsunomiya University School of Agriculture
キーワード	Automated Time Study, Productivity Modeling, Data Logger, Machine Connectivity, Big Data
Key word	Automated Time Study, Productivity Modeling, Data Logger, Machine Connectivity, Big Data