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.
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
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:
The analytics graph tools themselves include capabilities such as:
In applying the Cactus CCS and Analytics platforms it is possible to:
All to conclude on gain in the infrastructure
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.
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.
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.