When you work in train maintenance you realize that, no matter how good the maintenance you do to the trains is, every revenue service is a challenge! This is even more noticeable in legacy fleets, when the trains are older, you even have less feedback from the diagnostic tools. When we started developing monitoring tools and deploying them in the fleets, we were not blind anymore, we were able to see how the fleet was performing during service, and it was a relief for us, we started working better and more motivated, the customer satisfaction rate was highly increased and we could give a best service. Now, we not only deploy remote diagnostic systems for the fleet, but also create visual solutions for operators, customers (now they can see the train's occupation, coach temperature, speed, power consumed, etc.), apply machine learning algorithms to forecast how some system will perform depending in the environmental variables, etc. The highest motivation for me is knowing that there is still so many technology to implement in trains to improve their reliability and user experience for the customers!

When I joined Alstom, I started working as maintenance engineer in a legacy fleet. The trains were amazing and their reliability was top but they were a little bit old and the failure rate was a little bit higher than it used to. After deploying the monitoring system, we decided to create a well defined action plan: - Analyse all the events and failure modes detected by the monitoring devices. - Detect which of them are the ones impacting the fleet reliability, focus on the first 10 of the list. - Analyse which part of the train do these defects impact. - Perform specific action for each of the parts. - Analyse the impact in the fleet. By this new philosophy, we were able to detect which elements of the train were being degraded and impact the fleet reliability, and by overhauling or replacing them, the performance of the fleet was dramatically increased!