Separating the impact of work environment and machine operation on harvester performance
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ORIGINAL PAPER
Separating the impact of work environment and machine operation on harvester performance Lari Melander1 · Risto Ritala1 Received: 27 February 2020 / Revised: 15 June 2020 / Accepted: 26 June 2020 © The Author(s) 2020
Abstract In mechanized logging operations, interactions between the forest machines and their operators, forest resources and environmental conditions are multifold and not easily detected. However, increased computational resources and sensing capabilities of the forest machines together with extensive forest inventory data enable modeling of such relationships, leading eventually to better planning of the operations, better assistance for the forest machine operators, and increased efficiency of timber harvesting. In this study, both forest machine fieldbus data and forest inventory data were acquired extensively. The forest inventory data, acquired nationwide, was clustered to categorize general tree and soil types in Finland. The found forest categories were applied when the harvester fieldbus data, collected from the forest operations in the North Karelia region with two similar harvesters, was analyzed. When the performance of the machine and the operator, namely the fuel consumption and log production, is studied individually for each forest cluster, the impact of working environment no longer masks the causes based on the machine or the operator, thus making the observations from separate forest locations comparable. The study observed statistically significant differences in fuel consumption between the most general tree and soil clusters as well as between the harvester-operator units. The modeling approach applied, based on multivariate linear regression, finds such reasons for the differences that have clear interpretation from machine setup or operator working style perspective, and thus offers a feasible method for assisting the operators in improving their working practices and thus the overall performance specifically at forest of given type. Keywords Forestry · Data fusion · Machine learning · Forest data · Fieldbus data · Harvester · Performance
Introduction Forests resources are being digitalized throughout the world. Remote sensing in its many forms (see, e.g., Holopainen et al. 2014; Dash et al. 2016; White et al. 2016; Talbot et al. 2017) has been widely applied to provide tree and topographic data of forests, enabling better planning of forest operations. This is often referred to as precision forestry or Industry 4.0 in wood supply (Holopainen et al. 2014; Mason et al. 2016; Müller et al. 2019). Related to this trend, forest inventory data is collected worldwide, in particular in Communicated by Eric R. Labelle. * Lari Melander [email protected] 1
Automation Technology and Mechanical Engineering, Tampere University, Korkeakoulunkatu 10, 33720 Tampere, Finland
Europe, Canada, USA, Russia, Brazil, China and New Zealand (Tomppo et al. 2010). Furthermore, at least in the Nordic countries, the effort is to make forest data public (Kangas et al. 2018). I
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