Machines and devices nowadays produce large amounts of data that are typically
transmitted and stored on remote servers. This can lead to high data transfer
and storage costs, delays, as well as security and privacy issues. Data processing
at the edge of the network allows the data of sensors and devices to be analyzed
and processed in their immediate proximity, and only key information is sent to
the cloud for further processing.
The thesis describes a practical example of monitoring the effectiveness of
the production process by calculating the overall equipment effectiveness (OEE)
indicators at the edge of the network. The TwinCAT environment runs a production
line simulation based on the PackML state model. The TwinCAT device is
connected to a Raspberry Pi computer running a program written in the Python
programming language, which captures data from the production process and
calculates OEE indicators in real-time. These are then transferred to the Azure
IoT Central web application, where the operator has remote supervisory control
over the effectiveness of the process.
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