Field Setup and Assessment of a Cloud-Data Based Crane Scale (CCS) Considering Weight- and Local Green Wood Density-Related Volume References

Starke, Michael; Geiger, Chris (2021). Field Setup and Assessment of a Cloud-Data Based Crane Scale (CCS) Considering Weight- and Local Green Wood Density-Related Volume References Croatian journal of forest engineering, 43(1), pp. 29-45. University of Zagreb, Faculty of Forestry 10.5552/crojfe.2022.1186

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When investigating the forwarding process within the timber supply chain, insufficient data often inhibits long-term studies or make real-time optimisation of the logistics process difficult. Information sources to compensate for this lack of data either depend on other processing steps or they need additional, costly hardware, such as conventional crane scales. An innovative weight-detection concept using information provided by a commonly available hydraulic pressure sensor may make the introduction of a low-cost weight information system possible. In this system, load weight is estimated by an artificial neural network (ANN) based on machine data such as the hydraulic pressure of the inner boom cylinder and the grapple position. In our study, this type of crane scale was set up and tested under real working conditions, implemented as a cloud application. The weight scale ANN algorithm was therefore modified for robustness and executed on data collected with a commonly available telematics module. To evaluate the system, also with regard to larger sample sizes, both direct weight-reference measurements and additional volume-reference measurements were made. For the second, locally valid weight-volume conversion factors for mainly Norway spruce (Picea abies, 906 kg m-3, standard error of means (SEM) of 13.6 kg m-3), including mean density change over the observation time (–0.16% per day), were determined and used as supportive weight-to-volume conversion factor. Although the accuracy of the weight scale was lower than in previous laboratory tests, the system showed acceptable error behaviour for different observation purposes. The twice-ob-served SEM of 1.5% for the single loading movements (n=95, root-mean-square error (RMSE) of 15.3% for direct weight reference; n=440, RMSE=33.2% for volume reference) enables long-term observations considering the average value, but the high RMSE reveals problems with regard to the single value information. The full forwarder load accuracy, as unit of interest, was observed with an RMSE of 10.6% (n=41), considering a calculated weight-volume conversion as reference value. An SEM of 5.1% for already five observations with direct weight reference provides a good starting point for work-progress observation support.

Item Type:	Journal Article (Original Article)
Division/Institute:	School of Agricultural, Forest and Food Sciences HAFL School of Agricultural, Forest and Food Sciences HAFL > Multifunctional Forest Management School of Agricultural, Forest and Food Sciences HAFL > Multifunctional Forest Management > Forest Production
Name:	Starke, Michael0000-0002-3651-7664 and Geiger, Chris
Subjects:	S Agriculture > SD Forestry T Technology > TJ Mechanical engineering and machinery
ISSN:	18455719
Publisher:	University of Zagreb, Faculty of Forestry
Projects:	[UNSPECIFIED] Forwarder2020
Language:	English
Submitter:	Michael Starke
Date Deposited:	31 Jan 2022 14:57
Last Modified:	31 Jan 2022 14:57
Publisher DOI:	10.5552/crojfe.2022.1186
Uncontrolled Keywords:	crane scale, forwarder, cloud data, fleet management, timber logistics, artificial neural networks, green density, wood density
ARBOR DOI:	10.24451/arbor.16532
URI:	https://arbor.bfh.ch/id/eprint/16532