Pervasive environmental sensing for Industry 4.0 as an educational tool

Abstract

The reduced cost of implementing pervasive industrial sensing networks enables universities to incorporate these tools in engineering curricula. They provide engineering students from increasingly computerized backgrounds, such as mechanical and automotive engineering, the opportunity to work alongside students from technical schools who bring different skill sets than what students may be used to, synthesize historical data, and drive the sensing system’s physical system design and implementation. This paper outlines this convergent curriculum’s initial implementation stage, including the wireless environmental sensing Internet of Things (IoT) network, focusing on laboratory environmental sensing. Students placing many sensors around the lab and on equipment generates a wealth of real-time and historical data for use in the classroom and provides them a tangible example of learning to measure the world around them. This setup parallels the current varied Industry 4.0 state of the manufacturing industry, where Big Data exists but is underutilized, and where additional sensors and intelligent machine data streams are added each year. Students in each class are given a defined portion of a broader roadmap to a fully instrumented and intelligent laboratory environment. In the first step, student-programmed environmental sensors were placed around the lab and provide temperature, humidity, pressure, and gas mixture measures every five minutes. Classroom use of the aggregated data includes visualizing the laboratory and essential equipment’s current status using a Microsoft PowerBI dashboard and historical data visualization and analysis through trend forecasting and outlier detection in Python JupyterLab notebooks. The IoT system’s installation also provided an infrastructure for further study of future student-designed IoT projects.