The Internet of Things adoption in the manufacturing industry allows enterprises to monitor their electrical power consumption in real time and at machine level. In this paper, we follow up on such emerging opportunities for data acquisition and show that analyzing power consumption in manufacturing enterprises can serve a variety of purposes. Apart from the prevalent goal of reducing overall power consumption for economical and ecological reasons, such data can, for example, be used to improve production processes. Based on a literature review and expert interviews, we discuss how analyzing power consumption data can serve the goals reporting, optimization, fault detection, and predictive maintenance. To tackle these goals, we propose to implement the measures real-time data processing, multi-level monitoring, temporal aggregation, correlation, anomaly detection, forecasting, visualization, and alerting in software. We transfer our findings to two manufacturing enterprises and show how the presented goals reflect in these enterprises. In a pilot implementation of a power consumption analytics platform, we show how our proposed measures can be implemented with a microservice-based architecture, stream processing techniques, and the fog computing paradigm. We provide the implementations as open source as well as a public demo allowing to reproduce and extend our research.

中文翻译：

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制造企业用电数据分析的目标与措施

制造业中的物联网采用使企业能够在机器级别实时监控其电力消耗。在本文中，我们跟进了此类新兴的数据采集机会，并表明分析制造企业的功耗可以用于多种目的。除了出于经济和生态原因降低总功耗的普遍目标外，此类数据还可用于改进生产过程。基于文献综述和专家访谈，我们讨论了分析功耗数据如何服务于目标报告、优化、故障检测和预测性维护。为了实现这些目标，我们建议实施实时数据处理、多级监控、时间聚合、关联、软件中的异常检测、预测、可视化和警报。我们将我们的发现转移到两家制造企业，并展示所提出的目标如何反映在这些企业中。在功耗分析平台的试点实施中，我们展示了如何使用基于微服务的架构、流处理技术和雾计算范式来实施我们提出的措施。我们提供开源的实现以及允许复制和扩展我们的研究的公共演示。我们展示了如何使用基于微服务的架构、流处理技术和雾计算范式来实施我们提出的措施。我们提供开源的实现以及允许复制和扩展我们的研究的公共演示。我们展示了如何使用基于微服务的架构、流处理技术和雾计算范式来实施我们提出的措施。我们提供开源的实现以及允许复制和扩展我们的研究的公共演示。