The main focus and research field of this work is machine learning as a tool for classifying faults of machine elements. In the first part, we address the research, from which we take the raw data and the preprocessing of the gathered data set. The research takes a look at 5 different bearing faults: axial and radial overload, bending moment, contamination and shield defect. Next, we take a look at the theoretical background of machine learning, algorithms for analysis and examples of practical use. As an important aspect we research the possibilities of optimizing model parameters and evaluate the success of our predictions.
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