The concept of digital twins has captured the rail industry’s imagination, promising transformative insights through integrated data and advanced analytics. Yet for many networks, full-scale digital twin implementations remain out of reach due to high costs, complexity, and challenges with legacy datasets. A more practical and achievable alternative is the Maintenance Digital Twin—a focused application of digital twin technology that addresses key routine maintenance challenges such as track worker safety and resourcing. In today’s cost-constrained environment, railway networks are turning to solutions that automate measurements traditionally performed manually. This shift not only frees up critical maintenance resources for higher-value tasks but also significantly reduces safeworking costs.
The successful adoption of Maintenance Digital Twins hinges on two factors. First, early and ongoing engagement with subject matter experts ensures a deep understanding of current manual workflows and supports a considered transition to automated processes. Second, reliable and complete network reference data is critical to enabling automated workflows and ensuring the system’s success. By adopting a Maintenance Digital Twin, railway operators can achieve tangible efficiencies and improvements without the prohibitive costs and complexities associated with larger, overly complex digital twin initiatives.
Agonics collaborates with railway networks to uplift reference data and implement automated measurement workflows as part of their Maintenance Digital Twin strategy. These solutions integrate seamlessly into existing processes, resulting in lower safeworking costs and a renewed focus on reducing maintenance backlogs.