In the industrial world, ensuring safety and operational efficiency, along with constant performance improvement, is of great importance. To achieve these goals, constant measurement of the parameters, such as vibration and sound, and monitoring of the system’s behavior are necessary. This master thesis will focus on the performance of the rotary parts of the machinery. Traditionally, human inspection and manual assessment are used to outline conclusions about the behavior and condition of the machine. Testing of the rotary parts involves analyzing audio signals by manual assessment. This thesis will focus on the vibrations produced by these parts and investigate ways to optimize the assessment of rotating systems. Utilizing the numerous advantages of embedded systems, in this case, STM32 microcontrollers, this master’s thesis explores signal processing methods such as the fast Fourier transform and Morlet wavelet transform. The detailed approach to applying both methods to analyze the data from the rotating system is described. It shows that both methods are good for detecting defects in rotating machinery, and the decision on which method to choose depends on the nature of the vibration signal as well as the nature of the faults that may occur. If machinery faults manifest in the form of periodic signals, the fast Fourier transform is a better option because it is more efficient and better for real-time systems, but for non-periodic faults, the Morlet wavelet transform is preferred. Additionally, through experimental analysis, this thesis gives new ideas on where to put sensors on rotating machines to get the best results. It shows that the sensors should be mounted close to the vibration source, on a flat surface and in the direction of the vibrations. This thesis lays a solid foundation for automating fault detection in rotating machinery, showing how to collect and analyze data that can be used for future implementation of machine learning models for predictive maintenance.