RailComm: Insight™ monitoring and analytics platform

RailComm Insight™ is a smart monitoring and analytics platform for railroad infrastructure. RailComm is transforming traditional remote condition monitoring to a smart decision support tool for effective preventive and corrective condition-based maintenance. The platform marks the company’s steps towards the Internet of Things (IoT) with remote monitoring and predictive analytics for the rail industry.

With RailComm Insight, maintainers not only monitor the actual condition of equipment to determine when maintenance is needed, but sophisticated analytics determine if a failure might occur and notify them to proactively resolve issues. By preventing unexpected asset malfunction at the first sight of a performance deviation from normal operation, RailComm Insight helps freight and passenger railroads decrease unplanned delays to minimize their economic and social impact.

This software platform extracts data from field sensors and correlates it with external sources, such as yard and mainline control systems and weather, and automatically analyzes it using predictive algorithms on a cloud-based platform to determine if and when assets might fail. Keeping a high prediction rate is a key factor in the success of RailComm Insight, and this is achieved by enabling continuous machine learning with high volumes of aggregated data.

RailComm Insight’s intuitive Web user interface provides high-level and detailed views that are simultaneously updated to highlight developing problems. Users can subscribe to RailComm Insight’s automatic severity-based notifications, which provide actionable insights via email, text, voice call, or to a third party system via SNMP.

An example of an application supported by RailComm Insight is the remote condition monitoring and failure prediction for mainline switches. The platform is able to predict and detect malfunctions related to power source/battery, motor, lock rod, obstructions, and more. Maintainers not only benefit from this tool by preventing service downtime when alerted by the system, but they also are able to determine the efficacy of performed maintenance and whether maintenance or other track work is affecting its performance.

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