IoT based Industrial Predictive Maintenance

OData support
Supervisor:
Dr. Vida Rolland
Department of Telecommunications and Media Informatics

This thesis is written at the Electrical Engineering and Informatics

Faculty, Budapest University of Technology and Economics and is about the connection of the Industry 4.0 and the Internet of Things. The main subject is an IoT-based device, which is able to diagnose latent malfunctions in older industry machines by measuring their temperature changes.

The data collected by the device is sent to the processing server for data analysis and display. The server can analyze the data and, if necessary, notify the person in charge, who can correct the error before the malfunction.

Industrial failure predictions can be used to schedule maintenance considerably more efficiently, thus reducing costs.

This thesis describes the process of designing and implementing the device and server, the steps required to make such a device, and introduces the necessary technologies that are essential to complete a thesis and run a modern industrial failure predicting system. In addition to these, a number of practices are described which simplify the design process.

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