Big Data Challenge as Train Company Moves to Predictive Maintenance
Grant Gerke | February 2, 2017
There’s an interesting blog series on Trenitalia, a state-owned Italian train company, via ARC’s Industrie 4.0 website that depicts a transition from condition monitoring to a more predictive approach. The company reveals its real-time dashboards, but also discusses their transition to a component-based maintenance approach, which has many parallels to the factory or field space.
The scope is impressive. The new predictive application includes up to 4,000 “rolling stock” assets, with each locomotive collects up to 10,000 parameters per second. According to a news report, sensors will measure variables such as motor temperature, speed, traction, braking effort and line voltage.rt and line voltage.
More from the News report:
Sensor data is aggregated on-board through a remote PC or similar interface and offloaded via a communication gateway, typically via wi-fi when a train arrives at a station or at the maintenance plant. Data is sent to a Trenitalia data centre, and loaded into SAP HANA and cloud systems and dashboards for real-time monitoring, analysis and drill-down.
From ARC Advisory:
However, the team identified more representative KPI’s than mileage. These include door opening/closing cycles. They distinguished groups of components with higher or lower risk. With this information, Trenitalia is transitioning to a dynamic, component-based maintenance strategy in which higher risk components and components reaching the limits of their KPIs are checked and maintained more frequently; while other components are checked and maintained less frequently. In some cases, diverging KPIs of components on the same train can be balanced by choosing specific destinations. For example, trips causing more left wheel rotations and accelerations can be balanced with destinations leading to more right wheel accelerations. Trenitalia had to make its integrated travel and maintenance schedules much more granular to achieve the desired massive increase in reliability and savings.