Architecture of an Intelligent Vibration Diagnostics System for Vehicles and Methodology for Vibration Signal Analysis

Abstract

The article addresses issues related to developing methods for analyzing vibration signals and forming the architecture of an intelligent vehicle vibration diagnostics system. The relevance of the research stems from the need to enhance the reliability and safety of transportation systems amid digitalization and the implementation of cyber-physical technologies. The primary processing method chosen is spectral analysis based on the fast Fourier transform, which enables the identification of dominant frequency components and the formation of diagnostic features. An experimental implementation of vibration acceleration recording was carried out using multi-channel accelerometer sensors, and digital data processing was performed to calculate integral and spectral parameters. Based on this methodology, a concept for a multi-level architecture of an intelligent system was proposed, including sensor, analytical, and intellectual levels. This approach integrates with digital monitoring platforms and implements condition-based maintenance, increasing diagnostic efficiency and improving vehicle reliability. The developed approach can serve as a basis for advancing modern intelligent diagnostic systems in the transportation industry, capable of effectively preventing failures and increasing vehicle operational readiness.