Enhancement of Physical Phenomena Analyze Effectiveness using Artificial Intelligence Algorithms on FPGA

Abstract

The study of physical phenomena and the analysis of the behavior of dynamic systems are fundamental to applied and engineering physics, as many modern applications rely on a precise understanding of how mechanical and electrical systems respond to external changes. Among these phenomena, mechanical vibrations stand out as one of the most important physical indicators used to assess the operational condition of industrial systems, such as motors, pumps, turbines, and rotating bearings.

Physically, vibrations represent a system's response to external forces or internal disturbances, and can be mathematically described using differential equations that describe mass motion, damping, and stiffness. However, real-world systems are often multivariable and nonlinear, making their analysis using traditional analytical models of limited accuracy, especially in the presence of noise or unstable changes in operating conditions.

This research focuses on developing an integrated system for analyzing mechanical vibrations for predictive maintenance using artificial intelligence techniques implemented on FPGA boards. Vibrational signals are converted into digital characteristics representing the physical state of the system, and then passed to an AI model embedded within the hardware to make immediate decisions about the operating status.