Smart Suspension Systems for Sports Motorcycles: A Finite Element and IoT-Based Condition Monitoring Approach

Abstract

The increasing demand for high-performance motorcycles necessitates the development of advanced suspension systems that not only ensure rider comfort but also integrate intelligence for predictive maintenance. This paper proposes the design and finite element analysis (FEA) of a smart suspension system using lightweight materials combined with IoT-enabled sensors for real-time condition monitoring. Unlike conventional designs, the system employs magnetorheological dampers and aluminum alloys to optimize both ride dynamics and durability. Simulation results validate reduced structural stress and improved vibration damping. The integration of IoT sensors allows predictive fault detection, offering enhanced safety and prolonged service life for sports motorcycles.