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首页> 外文期刊>International Journal of Computer Integrated Manufacturing >Determination of the machine energy consumption profiles in the mass-customised manufacturing
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Determination of the machine energy consumption profiles in the mass-customised manufacturing

机译：测定大规模定制制造中的机器能量消耗型材

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摘要

This paper presents an original methodology and related algorithms that are dedicated to monitoring energy efficiency in discrete production stations. The learning phase of the algorithm is executed during the regular production process and is based on observations of the behaviour of the production station and energy consumption measurements. The collected information is then processed using data-mining procedures in order to find the clusters that reflect the energy consumption profiles that are specific for different variants of production. The profiles are used to monitor energy efficiency and detect anomalies. The main benefit of the proposed approach is its flexibility. No additional calibrating operations or technological knowledge are required. Although the presented proof by research results are focussed on pneumatic air installations, the proposed methodology can also be used for other media. The output of the proposed solution is a very precise estimation of energy consumption in reference to a given variant of production. It allows for the accurate detection of compressed air consumption anomalies. Such anomalies can be caused by technical (machine faults) and technological (human errors) problems. The proposed methodology can be applied for the optimisation of energy consumption and for the detection of machine maintenance problems that are visible through abnormal compressed air consumption.
机译：本文介绍了一种原始方法和相关算法，致力于监控离散生产站的能效。算法的学习阶段是在常规生产过程中执行的，并且基于生产站的行为和能量消耗测量的观察。然后使用数据挖掘过程处理收集的信息，以便找到反映特定于不同生产变体的能量消耗曲线的集群。配置文件用于监测能量效率并检测异常。建议方法的主要好处是其灵活性。不需要额外的校准操作或技术知识。虽然通过研究结果的卓越证据主要集中在气动空气装置上，但该方法也可以用于其他媒体。所提出的解决方案的输出是参考给定的生产变体的能量消耗的非常精确估计。它允许精确地检测压缩空气消耗异常。这种异常可能是由技术（机器故障）和技术（人类错误）问题引起的。所提出的方法可以应用于能量消耗的优化和通过异常压缩空气消耗可见的机器维护问题的优化。

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