Paper Title
Digital Twins for Real-Time Condition Monitoring and Predictive Lifecycles of Primary Springs Used in Suspension Systems

Systems condition monitoring is critical for maintenance, high efficiency, and predetermined design parameters. This paper proposes a new, cost-effective, straightforward Digital Twin Model (DTM) for Live Condition Monitoring (LCM) and Predictive Lifecycles. In this paper, the dimensions of the DTs modelling improved by reducing them from five to only three dimensions; Physical, Digital and Connection entities, with high accuracy and efficiency. The proposed DTM improves the empirical predetermined average load for simulation and experiment by 35.7 % (1.6 times more). Based on the actual load that the system experiences in the real-life case study, the DTM improves the empirical predetermined average lifecycles of the system by 12 times compared to the simulated results and nine times more compared to the experimental results. The proposed DTM still improves the average lifecycles of the system by 19.7% (1.2 times more) compared to the wireless DTM results based on the actual load applied to the system. A real-life case study of a suspension system in a Peugeot 3008 is used to demonstrate the proposed DTM’s high accuracy and efficiency. Keywords - Industry 4.0, Digital Twin, Predictive Maintenance, Condition Monitoring, Virtual Reality