Digital Twin-Based Structural Health Monitoring of Automotive Chassis for Real-Time Safety Enhancement

Abstract

Automotive chassis structures are subjected to severe, highly dynamic multi axial cyclic loading conditions, operational vibrations, and aggressive environmental corrosion throughout their operational lifecycle. Traditional structural health monitoring (SHM) paradigms rely heavily on periodic offline inspections, strain gauge instrumentation, or post-failure metallurgical analysis, which fail to capture cumulative fatigue damage progression under real-world stochastic operating conditions. This comprehensive review paper investigates the transformative integration of Digital Twin (DT) technology with automotive chassis SHM to enable continuous, real-time safety enhancement and proactive structural prognosis. We establish a rigorous architectural taxonomy connecting physical chassis sensor networks— comprising piezoelectric accelerometers, fiber Bragg grating (FBG) strain sensors, and multi-axis inertial measurement units (IMUs)—to cloud-edge synchronized cyber-physical models. Furthermore, we analyze advanced physics-based finite element analysis (FEA) models fused with data-driven machine learning algorithms (such as deep recurrent neural networks and Gaussian process regression) to achieve real-time stress field reconstruction and remaining useful life (RUL) estimation. The paper critically evaluates edge-cloud computing latency trade-offs, data synchronization challenges, and sensor fault tolerance in harsh vehicular environments. Finally, we outline prevailing research gaps, standardized validation methodologies, and future trajectories for autonomous, fail-safe vehicle architectures.

KEYWORDS: Automotive Chassis; Digital Twin; Structural Health Monitoring (SHM); Real-Time Prognostics; Finite Element Analysis; Sensor Fusion; Machine Learning.