The advancement of intelligent systems for the assessment and online monitoring of vehicles’ functional conditions, as well as for environmental monitoring and enhancing user operations, is essential to the progress of modern technology. Various types of noises, vibrations, and sound signals produced by different components of mechanical systems provide valuable information for evaluating the functional status of vehicle elements. However, challenges arise due to the diversity, ambiguity, non-linearity, and multi-dimensionality of information flow from dynamic systems, which constrain the capabilities of information technology. A promising approach to address these challenges is the parametric representation of the information signal from an object (such as electrical signals, sounds, or noises) within the context of probable dynamic events. The visualisation of acoustic signals, achieved by transforming them into a topological 3D model, presents new opportunities for developing remote monitoring methods for mechanical systems. This approach allows for analysing the condition and longevity of sound-emitting objects and early detection of potential failures. A technique is proposed for identifying mechanical defects based on noise parameters, which assesses the operational condition of a vehicle’s mechanical system. This is exemplified by the parametric visualisation of the acoustic noise signal from piston engines, showcasing differentiation in analysis.