Department of Industrial & Management System Engineering, Kyung Hee University, 1732, Deogyeong-daero, Yongin-si 17104, Korea.
Sensors (Basel). 2022 Apr 6;22(7):2817. doi: 10.3390/s22072817.
As the legislative pressure to reduce energy consumption is increasing, data analysis of power consumption is critical in the production planning of manufacturing facilities. In legacy studies, a machine conducting a single continuous operation has been mainly observed for power estimation. However, the production machine of a modularized line, which conducts complex discrete operations, is more like the actual factory system than an identical simple machine. During the information collection of this kind of production line, it is important to interpret mixed signals from multiple machines to ensure that there is no reduction in the information quality due to noise and signal fusion and discrete events. A data pipeline-from data collection (from different sources) to preprocessing, data conversion, synchronization, and deep learning classification-to estimate the total power use of the future process plan, is proposed herein. The pipeline also establishes an auto-labeled data set of individual operations that contributes to building an power estimation model without manual data preprocessing. The proposed system is applied to a modular factory, connected with machine controllers, using standardized protocols individually and linked to a centralized power monitoring system. Specifically, a robot arm cell was investigated to evaluate the pipeline, with the result of the power profile being synchronized with the robot program.
随着节能降耗的立法压力不断增大，对用电数据进行分析对于制造设施的生产计划至关重要。在以往的研究中，主要观察了执行单一连续操作的机器来进行功率估计。然而，模块化生产线的生产机器比实际工厂系统更类似于实际工厂系统，因为实际工厂系统执行复杂的离散操作，而不是相同的简单机器。在这种生产线的信息收集过程中，重要的是要解释来自多台机器的混合信号，以确保由于噪声和信号融合以及离散事件而不会降低信息质量。本文提出了一种从数据收集（来自不同来源）到预处理、数据转换、同步和深度学习分类的数据管道，以估计未来工艺计划的总功耗。该管道还建立了一个自动标记的单个操作数据集，有助于构建无需手动数据预处理的功率估计模型。该系统应用于一个模块化工厂，通过使用标准化协议分别连接机器控制器，并链接到集中式功率监控系统。具体来说，研究了一个机器人手臂单元来评估该管道，结果表明功率曲线与机器人程序同步。
