Nowdays, we have more and more sensors and smart edge devices around the world, thanks to the rapid developments in the Industry 4.0. As sensors have become smaller, cheaper and more powerful, there is growing interest in predictive maintenance in industry.
The aim of this diploma thesis is to describe nad present machine learning algorithms that can be used in the design of predictive maintenance technologies. Hardware and software products that are available on the market are presented and then one of the technologies is described in more detail and is then used in an experiment.
In the introductory chapters of the thesis, the development of maintenance techniques and prediction models for predictive maintenance are presented. This is followed by an introduction to machine learning and its different categories. This chapter goes on to describe in more detail the most commonly used algorithms in these categories. It is then followed by an overview of some of the hardware and software solutions that are currently available to users. At the end, I decided to use one of the solutions, the iComox smart box and I used it in an experiment. The test of the equipment was carried out in a home environment and is described in detail. The conclusion concludes with an opinion on whether the choice of this device was reasonable for the chosen experiment.
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