Combining Different Data Sources for IIoT-Based Process Monitoring

Abstract

Motivation—Industrial internet of things (IIoT) refers to interconnected sensors, instruments, and other devices networked together with computers’ industrial applications, including manufacturing and energy management. This connectivity allows for data collection, exchange, and analysis, potentially facilitating improvements in productivity and efficiency, as well as other economic benefits. IIoT provides more automation by using cloud computing to refine and optimize process controls. Problem—Detection and classification of events inside industrial settings for process monitoring often rely on input channels of various types (e.g. energy consumption, occupation data or noise) that are typically imprecise. However, the proper identification of events is fundamental for automatic monitoring processes in the industrial setting, allowing simulation and forecast for decision support. Methods—We have built a framework where process events are being collected in a classic cars restoration shop to detect the usage of equipment such as paint booths, sanders and polishers, using energy monitoring, temperature, humidity and vibration IoT sensors connected to a Wifi network. For that purpose, BLE beacons are used to locate cars being repaired within the shop floor plan. The InfluxDB is used for monitoring sensor data, and a server is used to perform operations on it, as well as run machine learning algorithms. Results—By combining location data and equipment being used, we are able to infer, using ML algorithms, some steps of the restoration process each classic car is going through. This detection contributes to the ability of car owners to remotely follow the restore process, thus reducing the carbon footprint and making the whole process more transparent.