Cactus Rail - Cactus Analytics

Cactus Analytics

Analytics. Another way to interpret information.

The industry is shifting from traditional mileage-based, preventative maintenance regimes, and pivoting towards data-driven, condition-based, predictive maintenance that allows infrastructure companies to reduce the life-cycle costs of critical assets and components on the track.

Every infrastructure owner can benefit from Cactus Analytics for measuring operations with the ability to define key indicators for supervision, condition-based asset management and continuous improvement.


Just another way to look at the information

In applying the Cactus CCS integration platform and the Analytics toolset, a lot of new insights are created without any need for installation of additional sensors in the infrastructure


And good means to visualize it understandably

Through our Analytics framework, component groups (points, level crossings, etc.) can be visualized as over time, with each other, and correlated with other data from the Cactus CCS platform, in order to answer questions such as:

  • What components have the best operational data, with regards to purchase cost and amount of maintenance?
  • How does weather and traffic intensity correlate with the functionality of the track-side objects?

The analytics graph tools themselves include capabilities such as:

  • Clustering (group data to search for similarities).
  • Correlation. Compare anything with anything.
  • Outlier and trend detection.


Conclude on gain

In applying the Cactus CCS and Analytics platforms it is possible to:

  • Measure performance before change.
  • Implement changes.
  • Measure performance after changes.

All to conclude on gain in the infrastructure


Track Circuits
  • Normal distribution occupancy time with deviation analysis
  • Overspeed
  • Transients

Based upon analytics and real-time monitoring data from existing TMS and SCADA/DCS,Cactus Rail refi nes data to improve the rail infrastructure – without any need for additional sensors.


Example of graph visualization

This graph shows a point with undetected, abnormal switch time, 15 seconds during 30 days, this entailed a “run-to-failure” approach that created a disturbance of traffic and a higher cost for getting back in operation. Detection of swich time is done by using already existing “dark data” from the traffic management system.


Asset management and predictive maintenance

Good asset management practice is crucial to mitigating potential costs in overrun penalties, especially as shifting requirements can derail even the most effective planning.

A principle illustration of possibilities in maintenance activities by applying analytics.


Being proactive with maintenance leads to continuous improvement to workflows, increased uptime and reduced spending on unnecessary repairs.


Predictive maintenance, PdM, relies on conducting maintenance based on trends within equipment data. This technology is tied to Condition-Based Monitoring, CBM, real/time systems for reading the output (condition) of an asset’s variables. Predictive maintenance is based on predicting when an asset needs attention rather than simply replacing a part when it could have lasted longer.



How can we …

  • “… upgrade the maintenance process and effectively turn it into track-side operations to reduce manpower on the line?”
  • “... harness technology to achieve data-driven planning and decision?”
  • “… streamline maintenance regimes and manage degrade?”
  • “… improve maintenance efficiencies and understand proven tactics to leverage technology to quickly identify points of failure and improve, data-driven, decision-making processes to ensure maintenance is done on time?”
  • “… compare maintenance operators in the light of running traffic operations most efficiently?”