Е-Публикации
Технически университет - София

Abstract: This paper focuses on the use of mobile phone audio recordings in various mechanical operations for a class of machine tools. The aim is to create a general approach applicable to diverse types of machines and the electrical drives that make them up by using cost-effective ways to capture acoustic data. It evaluates how variations in microphone distance and placement affect the acoustic characteristics of the recordings, and they are analyzed to assess the impact of these variables. The audio recordings are converted into visual representations using the Symmetrized Point Model (SDP) method, which converts sound into image-based data for processing by machine learning. This methodology offers a costeffective, scalable solution for real-time monitoring and fault detection in industrial environments and predictive maintenance.

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