Cohort: 1 

Location: London, United Kingdom 

Application Title: AI-powered digital asset management platform for rail operations and maintenance 

Team Members: Uchikado San (executive sponsor), Koji San, Nadia Mazzino

The challenge 

Rail networks operate under increasing pressure. Urban growth, climate commitments and rising expectations for reliable public transport are driving demand, while budgets remain constrained and infrastructure continues to age. Much of a rail operator’s cost base is tied to maintaining physical assets such as tracks, rolling stock and signalling systems, many of which are designed to operate for decades. 

Traditionally, maintenance relies on two main approaches. Reactive maintenance addresses faults after failures occur, often resulting in service delays and unplanned interventions. Planned maintenance follows fixed schedules based on time or mileage, regardless of the actual condition of assets. While predictable, this approach can lead to unnecessary replacements and inefficient use of resources. 

Some operators have introduced predictive tools using individual sensors, but these systems usually monitor components in isolation. Without a network-wide view, it remains difficult to prioritise interventions or understand how issues in one area affect overall performance. Overhead lines can span hundreds of kilometres in both populated and rural areas. Infrastructure managers must work hard to maintain this infrastructure which can be subject to weather damage, for example, which can result in potential faults. Inspections rely heavily on visual checks by drivers or maintenance teams, which are labour intensive.

The solution 

A unified platform for condition-based rail maintenance 

Hitachi Rail developed an AI-powered digital asset management platform to support a shift from schedule-based maintenance to condition-based decision-making. 

The platform integrates data from trains, signalling equipment and track infrastructure into a shared operational view. By analysing this data continuously, the system helps operators and maintenance teams identify emerging issues earlier and plan maintenance based on actual asset condition rather than fixed intervals. 

The objective is not to replace existing maintenance practices overnight, but to support more targeted interventions that reduce disruption and lower long-term costs. 

How it works 

From sensor data to operational insight 

Sensors installed on trains and along the network collect data on asset behaviour during normal operations. This includes vibration, temperature and electrical signals, depending on the component being monitored. Edge computing processes time-sensitive data close to the source, enabling fast detection of anomalies, while cloud-based analytics support system-wide analysis and forecasting. 

A digital representation of the rail environment is built to simulate how assets behave over time. This allows operators to test scenarios, anticipate degradation and plan responses more effectively. For example, wheel bearing maintenance previously relied on precautionary replacement. Thousands of bearings were changed at fixed intervals to avoid failure. With condition monitoring in place, replacements are now based on measured wear, reducing waste and downtime. 

The same approach applies to overhead line equipment. Image recognition and sensor data help identify faults before they escalate, reducing the need for manual inspection in difficult or hazardous locations. 

How it differs from conventional approaches 

Moving beyond isolated monitoring 

Conventional predictive maintenance tools focus on individual components and often operate as standalone systems. This limits their usefulness when decisions must be made across a complex network. 

Hitachi Rail’s platform integrates asset data across trains and infrastructure, allowing maintenance decisions to be prioritised at the network level. By combining local processing with central analysis, the system supports both rapid response and longer-term planning. This reduces reliance on blanket replacement strategies and reactive repairs. 

Impact 

The adoption of HMAX has delivered measurable value, including up to 15% reduction in maintenance costs and 15% lower energy consumption.

Why it matters 

Supporting reliable rail operations at scale 

As rail networks expand and infrastructure continues to age, maintenance strategies need to become more precise without increasing operational risk. Hitachi Rail’s approach shows how integrated asset data and AI-supported analysis can help operators focus effort where it is most needed. 

By shifting maintenance decisions closer to actual asset condition, the platform supports more reliable service delivery while making better use of existing infrastructure and maintenance resources.