The SmartVision™ Remote Diagnostics function enables operators to receive diagnostics information from their rolling stock fleets and visualize it in real-time. The control center, work planners and maintenance staff are continuously informed about the status of the fleet and potential technical issues. The staff can take actions instantly to fix the problems, limit their impacts on the operations as well as give the proper instructions to the driver even before he notices the problem.
Maintenance activities are expedited as the service personnel receives in advance information about issues which need actions. Thus, the maintenance scheduling and resource allocations are performed much faster, reducing service disruption and increasing both safety and operational efficiency.
SmartVision™ Condition Monitoring is used for detecting changes in the condition of such devices, for which Condition-Based Maintenance (CBM) shall be applied. The basic principle is to analyse and monitor changes in signals, which can be obtained from
The technically and commercially optimal solution for each individual fleet type is defined together with our customers in order to best support them in their transition towards CBM.
SmartVision™ Condition Monitoring can be used to monitor the condition of the rolling stock, the track or both of them.
The condition monitoring and predictive analytics functionalities of SmartVision™ build upon Humaware’s Adaptive Anomaly Detector; a unique data-driven toolset that provides automatic, reliable and early alerts.
Its features include:
SmartVision™ Predictive Analytics takes condition monitoring one step further by using advanced algorithms and analytics to predict likely remaining useful life of units. The SmartVision™ prognostics capability is provided by Humaware (an EKE Company).
The remaining useful lifetime estimates can be calculated for those components where the historical data collected from the vehicles indicate that the condition degrades against time or service cycles.
Predictive maintenance will enable optimisation of maintenance costs – when performing traditional preventive maintenance, servicing parts too early may unnecessarily shorten parts lifetime and increase costs. Conversely, if waiting too long, the component may experience failure and incur costly corrective maintenance and possible disruption in operations. Predictive maintenance addresses this by estimating remaining useful life using risk-based analysis techniques to optimise the time of maintenance.
