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

IF 2.7 2区 农林科学 Q1 FORESTRY Croatian Journal of Forest Engineering Pub Date : 2021-11-10 DOI:10.5552/crojfe.2022.1186
M. Starke, C. Geiger
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引用次数: 1

Abstract

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-observed 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.
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考虑重量和当地绿木密度相关体积参考的基于云数据的起重机尺度(CCS)的现场设置和评估
在调查木材供应链中的运输过程时,数据不足往往会阻碍长期研究或使物流过程的实时优化变得困难。弥补这种数据不足的信息源要么依赖于其他处理步骤,要么需要额外的、昂贵的硬件,如传统的起重机秤。利用普遍可用的液压传感器提供的信息的一种创新的重量检测概念可能使引入低成本的重量信息系统成为可能。在该系统中,基于臂架内缸的液压压力和抓斗位置等机器数据,采用人工神经网络(ANN)对载荷进行估计。在我们的研究中,这种起重机规模在实际工作条件下进行了设置和测试,并作为云应用程序实现。因此,权重尺度人工神经网络算法被修改为鲁棒性,并在使用常用的远程信息处理模块收集的数据上执行。为了评估该系统,也考虑到更大的样本量,进行了直接的重量参考测量和额外的体积参考测量。其次,确定挪威云杉(Picea abies, 906 kg m-3,平均标准误差(SEM)为13.6 kg m-3)的本地有效重量-体积转换因子,包括观测时间内的平均密度变化(每天-0.16%),并将其用作支持重量-体积转换因子。虽然重量秤的精度低于以前的实验室测试,但该系统在不同的观察目的下显示出可接受的误差行为。单次加载运动(n=95,均方根误差(RMSE)为15.3%)的二次观测SEM为1.5%,作为直接重量参考;n=440, RMSE=33.2%(体积参考),可以考虑平均值进行长期观测,但高RMSE暴露了单值信息方面的问题。考虑到计算的重量-体积转换为参考值,作为感兴趣的单位,货代满载精度的RMSE为10.6% (n=41)。对于已经有5个直接权重参考的观测值,5.1%的SEM为工作进度观测支持提供了良好的起点。
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来源期刊
CiteScore
5.20
自引率
12.50%
发文量
23
审稿时长
>12 weeks
期刊介绍: Croatian Journal of Forest Engineering (CROJFE) is a refereed journal distributed internationally, publishing original research articles concerning forest engineering, both theoretical and empirical. The journal covers all aspects of forest engineering research, ranging from basic to applied subjects. In addition to research articles, preliminary research notes and subject reviews are published. Journal Subjects and Fields: -Harvesting systems and technologies- Forest biomass and carbon sequestration- Forest road network planning, management and construction- System organization and forest operations- IT technologies and remote sensing- Engineering in urban forestry- Vehicle/machine design and evaluation- Modelling and sustainable management- Eco-efficient technologies in forestry- Ergonomics and work safety
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