Technical Specifications

The system is provided as railway appliances composed of touch panel computers, software and communication devices. Recorded data can be used for instant visualization of important train functions and fed to a remote data warehouse.

The system was originally designed to replace old monitoring devices with more than 20 years of service but has since evolved to become an important piece of a new paradigm in train operations: real-time monitoring and condition based maintenance supported by big data analytics.

Besides being compliant with legacy train environments, the system adds important new features such as GPS and Mobile Data Technologies (3G/4G) capabilities thus paving the way for location-based analysis, remote monitoring and real-time data feeding. It can therefore be used to provide old rolling-stock with the latest monitoring technology, putting it up to date with modern train systems.

Life 4.0 Trains was developed by HOLOS and is now being delivered in a Portuguese train operator.

Since the appliances can be set as a regular client to the train’s internal communication system, it does not interfere with any of the train’s operating functions. The progressive installation of such units allows the train operator to collect important data regarding the train’s operation and have it transmitted to a central data warehouse in an “online” or “offline” basis.

The main benefits of adopting this new paradigm regarding train transport are:

  • Real-time GPS tracking of the rolling-stock. It means the operations can have each individual train tracked in a Web map, for instance;
  • Regular events, warnings and technical parameters (such as instant speed or power consumption) can also be geolocated, which provides a helpful guide, for instance, while investigating the common source of problems;
  • Data mining operations over collected train’s information, bring new and qualified insights into trains’ maintenance operations. Correlation of data from multiple trains can lead to the detection of problems, such as line stretching or power-related problems;
  • Another possible use of big data would be in forecasts to predict arrival times or trigger maintenance alerts for example;
  • Provide a powerful tool for Condition-Based Maintenance (CBM) activities, focused on reaching the optimum balance between minimizing operating costs, maximizing fleet availability while ensuring compliance with regulations. Intransit detection of problems and its transmission to the maintenance yards allows predictive maintenance or in-track maintenance planning to ensure the highest service levels to the passengers;
  • To provide new data for cost analysis regarding operations;
  • Bring existing locomotives to state-of-the-art integration technology without disruptive changes in the underlying control system’s technology.