Goals and Measures for Analyzing Power Consumption Data in Manufacturing Enterprises

TL;DR

This paper explores how analyzing real-time power consumption data in manufacturing can improve efficiency, fault detection, and process optimization, using a microservice architecture and fog computing, with open source tools.

Contribution

It introduces a comprehensive set of measures and a microservice-based architecture for power data analysis in manufacturing enterprises, validated through pilot implementations.

Findings

Effective real-time monitoring and anomaly detection demonstrated

Power consumption analysis supports fault detection and predictive maintenance

Open source platform enables reproducibility and extension

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. 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.