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Using Horus’s many sensors to create a comprehensive profile of the rail corridor for proactive condition monitoring via general daily operations.
Regular monitoring of infrastructure is critical to ensuring the asset can support safe and reliable train operations. Identifying asset condition defects before failure enables the network maintainer to avoid potential disruptions to train operations and subsequent revenue loss.
Horus’ selected sensor integration can monitor the condition of rail infrastructure as fitted trains move across a network. The system has proven to detect a variety of potential track deformities during live operations, which has provided valuable information to our clients in accurately locating areas that need specific attention complete with location accurate information. This feature contributes to saving operational costs both from the allocation of resources and reduced impact to operations, as well as reducing the need for staff to be working in the rail corridor.
Horus is ideal for track condition monitoring as detailed accelerometer data is collected every time a train carries out its journey, allowing Horus to monitor vibrations for abnormalities along the operated track. Global Navigation Satellite Systems (GNSS) and Global Positioning Systems (GPS) data is used to provide exact coordinates on each deformity or track condition instance, which is logged and can then be sent back to nominated staff for actioning. In subterranean areas, triangulation is used to calculate the vehicles position which provides the location information in these environments.
When Horus is being used for track condition monitoring, the system may identify track conditions such as:
The reliability and accuracy of such detections is dependent on many factors, but in reasonable conditions, can provide very useful infrastructure monitoring data.
Back-end Office Alerts: Automated notifications of potential faults and reports can be sent to network control personnel, as configured by business rules.
Predictive Analytics: By using analytics and machine learning, Horus can build a picture over time of potential weak spots across a network by cross-correlating data from multiple train journeys.