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Time-Motion Analysis of Forage Harvest:A Case Study

机译:牧草收获的时间 - 运动分析 - 以案例研究

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摘要

Forage harvest is a time and energy intensive process requiring the coordination of multiple pieces of equipment. Detailed characterizations of the time spent in each work state for each piece of equipment would increase the understanding of process inefficiencies and aid in development of optimization tools. Geospatial and controller area network (CAN) machine data were recorded on forage harvesters and transport equipment, during two types of harvest operations, to quantify utilization of harvesters and transports as well as transport productivity. The data collection and processing method was successful in identifying work states for forage harvesters and transports. The results indicated that overall utilization of the harvester for harvesting was 61% and dependent on transport availability. The portion of total operational time spent in the idle work state (idle utilization) was 10% to 20% for transports and 18% to 23% for harvesters. A new metric for transport productivity was developed and found to be highly dependent on transport capacity ranging from 125 to 49 Mg km h'1for semi-trucks and smaller transports, respectively. The proposed data collection methods and productivity metrics could be used to optimize the forage harvest process toreduce idle time and maintain crop quality.
机译:饲料收获是需要协调多个设备的时间和能量密集型过程。对每个设备的每个工作状态所花费的时间的详细表征将增加对过程效率低下的理解,并有助于开发优化工具。地理空间和控制器区域网络(CAN)机器数据记录在牧草收获者和运输设备上,在两种类型的收获操作中,以量化收割机和运输的利用以及运输生产率。数据收集和处理方法成功地识别牧草收割机和运输的工作状态。结果表明,收割机的总体利用率为61%,取决于运输可用性。在空闲工作状态(空闲利用率)中所花费的总操作时间的部分为运输的10%至20%,收割机的运输量为18%至23%。开发了一个新的运输生产率的公制,发现高度依赖于从125到49 mg km H'1的运输能力分别为半卡车和较小的运输。所提出的数据收集方法和生产率指标可用于优化牧草收获过程Tourdoreduce闲置时间并保持作物质量。

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