International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
www.irjet.net
Digital Twin-Based Fault Localization in Vehicle Braking Systems: A Data-Driven Approach Kiran Gurav1 1Kiran Gurav Student, Dept. Electronics and Telecommunication Engineering, KIT's College of Engineering
Kolhapur (Empowered Autonomous), Maharashtra, India -------------------------------------------------------------------------***-------------------------------------------------------------------------
Abstract - This paper presents the development and
Digital Twin technology offers a promising paradigm shift in this domain. By creating a dynamic virtual representation of a physical system, synchronized through real-time data, Digital Twins enable continuous monitoring, analysis, and prediction of system behaviour and health. In the context of vehicle systems, recent developments have demonstrated the effectiveness of simulation-based Digital Twin models for condition monitoring and diagnostics [3][4]. These systems leverage sensor data and simulation environments like MATLAB Simulink to replicate real-world scenarios for proactive fault detection and system evaluation [1][3].
simulation of an intelligent brake health monitoring system using MATLAB Simulink with Raspberry Pi integration. The system monitors three key parameters—temperature (via LM35 sensor), brake fluid level, and brake pad wear to assess the real-time condition of a vehicle's braking system. Sensor inputs are scaled, processed, and compared against predefined thresholds to detect faults like overheating, low fluid levels, and excessive brake wear. Based on the evaluation, appropriate warning messages are displayed, and an emergency actuation mechanism is simulated using a DC motor model.
This paper explores the application of a Digital Twin framework for fault localization within a vehicle's electronic braking system, adopting a data-driven approach implemented primarily through MATLAB simulations. The core focus of this work is to utilize readily available sensor data—particularly from the brake fluid pressure sensor—to detect anomalies indicative of common but critical faults, such as hydraulic fluid leakage or excessive brake pad wear. Fault localization is achieved by comparing sensor data against the simulated behaviour of the braking system, using threshold-based and data-driven techniques [2][5].
The Simulink model provides a dynamic visualization of sensor behaviour under varying conditions and helps validate the system’s response. The use of Raspberry Pi for real-time data interfacing supports portability and future hardware expansion. This approach reduces reliance on manual inspection, improves maintenance efficiency, and enhances vehicular safety through timely diagnostics. The system demonstrates the potential of embedded technologies in predictive maintenance applications for smart automotive systems. Key Words: Digital Twin, Automotive Safety, Brake Fluid Pressure, Digital Twin, Fault Localization, MATLAB Simulation, Predictive Maintenance.
This study demonstrates the feasibility and effectiveness of using MATLAB-based simulations to develop and test a Digital Twin for braking system diagnostics. The proposed approach provides a cost-effective and scalable solution that can enhance real-time safety diagnostics in modern vehicles. The subsequent sections detail the system modelling approach, the MATLAB simulation setup for normal and faulty conditions, the fault detection logic embedded within the Digital Twin, and the results validating its ability to localize pressure-related faults.
1. INTRODUCTION The braking system is arguably one of the most critical safety components in any vehicle. Its reliable operation is paramount to preventing accidents and ensuring occupant safety. As vehicles evolve with increasingly complex electronically controlled braking systems—such as Anti-lock Braking Systems (ABS) and Electronic Stability Control (ESC)—the challenge of ensuring their continuous health and diagnosing potential faults proactively becomes more significant. Traditional maintenance schedules and reactive diagnostics often identify problems only after performance degradation or failure has occurred, potentially compromising safety. There is a growing need for advanced diagnostic and prognostic methods that can predict and localize faults before they become critical [1][2].
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2. PROBLEM STATEMENT The reliability of automotive braking systems is non-negotiable for vehicle safety, yet conventional diagnostic methods often fall short in providing timely warnings for potentially critical failures. Reactive maintenance strategies and periodic inspections may fail to capture incipient faults like low brake fluid (indicated by pressure loss or temperature anomalies) or excessive wear and tear on brake pads (indicated by temperature
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