CASE STUDY
Digital Twin Feasibility Study
Capability: Systems Engineering
3 min read
CASE STUDY
Digital Twin Feasibility Study
Capability: Systems Engineering
3 min read
What?
RC Fornax was tasked with assessing the feasibility of implementing a Digital Twin capability to support predictive maintenance for the suspension system on a multi-purpose armoured land vehicle. The objective was to understand failure behaviours, sensor requirements, data-flow needs, and architectural implications to determine whether a Digital Twin could provide actionable condition-based maintenance insights.
How?
The initial step was to conduct a failure mode analysis that Collated and interrogated Design Failure Mode and Effects Analysis (DFMEA) data from existing platform documentation, identified critical suspension failure modes, degradation patterns, and environmental stressors, and mapped failure signatures to measurable physical parameters to inform sensor-level requirements.
By applying a systems thinking approach, RC Fornax defined the end-to-end architecture required to enable Digital Twin functionality based upon the Sensor Suite Edge Processing , the IoT Hub & Cloud Environment, and an Insight & Analysis Layer for data modelling, historical comparison, anomaly detection, and predictive maintenance workflows.
This architecture linked physical system behaviour with the digital model, enabling the foundations for condition prediction and remaining-useful-life assessments.
Result
RC Fornax successfully delivered a viable proof of concept Digital Twin demonstrating that predictive maintenance for the suspension system is both technically feasible and operationally valuable. The accompanying strategy and recommendations provided the customer with a clear, structured roadmap for Digital Twin development, scaling, and adoption across wider platform fleets.
