INNOVATIVE METHODS OF DIAGNOSTIC BRAKING SYSTEMS IN VEHICLES

Abstract

Modern vehicle manufacturers are actively implementing new technologies to improve the safety of their products. The main emphasis is on the implementation of self-diagnostic methods that notify the user of potential malfunctions. In vehicles (hereinafter referred to as vehicles) of the premium segment, integration with virtual assistants is offered, which can automatically send requests for repair or diagnostics to authorized service centers.

From the point of view of the safety of a car or truck, the health of the brake system is particularly important. Most of them are equipped with only basic sensors, such as a brake fluid level sensor and a brake pad wear indicator, which does not provide sufficient information to timely warn of potential malfunctions, such as wear or damage to brake discs, calipers or hubs. For the diagnosis of such cases, it is important to take into account the temperature and vibration indicators of the brake system elements, since they can indicate the presence or development of problems. According to research, brake system malfunctions are one of the main causes of road accidents.

The study is dedicated to the development of an innovative diagnostic system for the braking unit of a vehicle using modern technologies, such as intelligent sensors for measuring temperature and vibration, as well as machine learning algorithms. This paper examines the system’s architecture, which consists of a hardware component, a server platform for data processing, and a user interface. Special attention is given to the signal processing procedure, which includes centering, filtering, and transforming the input signal into the frequency domain, enabling the extraction of relevant signal components for further analysis.

Three approaches to processing input data from the vehicle are proposed: cloud-based processing, local processing, and a hybrid approach. Emphasis is placed on clustering efficiency and anomaly detection in data streams to improve diagnostic accuracy. The system allows the user to be promptly informed about vehicle malfunctions via a mobile application or the vehicle’s information console, contributing to the overall safety of the vehicle. The article also analyzes the possibility of applying the FMEA method to assess risks and the effects of failures.