# Towards the Internet of Smart Trains:
## 5 The Rise of the Internet of Trains
#### Industrial IoT Developments in the Rail Industry  
Renowned commercial companies have been investing recently in IIoT. Next paragraphs outline opportunities for the railway ecosystem (i.e., technology vendors and operators), including some maintenance and monitoring initiatives that today are making the smart railway ecosystem vision a reality. Trenitalia's Frecciarossa is working with SAP to develop a Dynamic Maintenance Management System (DMMS) [60]. The system presents cost savings between 8% to 10% of its maintenance bill. In this case, hundreds of sensors collect data in real time (from braking systems to the sliding doors), uploading them into SAP's cloud every ten minutes. Trenitalia runs 8000 trains per day using a fleet of 30,000 locomotives, coaches, and freight cars. Once the data are in the cloud, they are analyzed using SAP Predictive Maintenance and Service software and they are processed by the predictive analytics tool SAP HANA. Thus, Trenitalia can build predictive models using machine learning and also trigger actions (for example, when engine temperature hits a particular threshold, to help keeping trains running without delays). Key metrics, and diagnostic and management data are accessible by engineers and are visualized in real-time: the number of trains that are out of service, alerts that imply a maintenance action, the status of trains on the track, or the number of passengers. DMMS will be fully up and running across all Trenitalia's rolling stock in 2018 and it will generate a full petabyte of data annually. Next, Trenitalia is hoping to automate the few remaining parts of diagnostics and maintenance that cannot be spotted by sensors, such as the roof and undercarriage of the trains, which still need a visual inspection. In the future, these tasks will be automated using cameras, instead of the visual inspection required today. In 2013, the Finnish state-owned railway company [61], in order to improve its competitiveness, started to embed sensors into its systems to monitor possible failures related to the weather conditions. Previously, such a company performed maintenance in two ways. First, there was a scheduled maintenance that affected the most critical systems (e.g., bogies and wheels). Due to this type of maintenance, parts were replaced even when they still could be used more time. The second maintenance procedure consisted of fixing parts after they broke down. This kind of maintenance could not be predicted easily and derived in missed routes and unsatisfied customers. In order to prevent the problems that arose from these two maintenances procedures, the company developeda predictive maintenance program that monitors the state of the most relevant parts constantly. This system estimates through mathematical models when a part is likely to fail, so that it can be replaced before to avoid unplanned downtimes. To optimize the time between maintenance events, the company analyzes the data collected through a Statistical Analysis System (SAS) and determines if critical elements like the turning wheels or the wheel-and-axle sets need to be replaced. With all the improvements carried out, the company estimates that maintenance work will be reduced by 35 %. Moreover, since the cause of a failure can be identified more easily, the reliability of the trains is enhanced and savings are obtained in terms of time and money. Furthermore, the knowledge obtained through the system allows the company to minimize their spare part stock, buying and keeping only what is predicted that will be needed in the near-future. Predictive maintenance is also encouraged by Siemens together with Teradata [62]. They expect predictive maintenance will evolve towards next-generation maintenance, creating a whole new business model to provide completely new services with up-time guarantees, risk-sharing models, and performance-based contracts for mobility systems.