Abstract
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. In two industrial pilot cases, we discuss how analyzing power consumption data can serve the goals reporting, optimization, fault detection, and predictive maintenance. Accompanied by a literature review, we propose to implement the measures real-time data processing, multi-level monitoring, temporal aggregation, correlation, anomaly detection, forecasting, visualization, and alerting in software to tackle these goals. 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 show case allowing to reproduce and extend our research.
Highlights
The immense electrical power consumption of the manufacturing industry (International Energy Agency 2019) is4 wobe-systems GmbH, Edisonstraße 3, 24145 Kiel, Germany a considerable cost factor for manufacturing enterprises and a serious problem for environment and society
We show how our proposed measures can be implemented in an Industrial DevOps analytics platform, the Titan Control Center (Henning and Hasselbring 2021)
Our literature review shows that the goals reporting and optimization are subject of research in various disciplines, whereas fault detection and predictive maintenance based on industrial power consumption are still in an early stage
Summary
The immense electrical power consumption of the manufacturing industry (International Energy Agency 2019) is. We present the goals our studied enterprises aim at to achieve by analyzing power consumption data and propose a set of software-based measures that serve these goals. Our literature review shows that the goals reporting and optimization are subject of research in various disciplines, whereas fault detection and predictive maintenance based on industrial power consumption are still in an early stage. 2. Based on suggestions from the domain experts in our Titan project and results of our literature review, we propose the measures real-time data processing, multi-level monitoring, temporal aggregation, correlation, anomaly detection, forecasting, visualization, and alerting.
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