Trending Predictive Wayside Detector Data to Reduce Asset Failures
To mitigate safety risks and disruptions associated with railcar roller bearing failures, railways use wayside detectors to collect data about the condition of bearings (including temperature and vibration) on passing trains. While these data-acquisition systems are common within the North American rail industry, CP is the first railway to analyze data trends to develop a predictive model designed to further improve the safety and reliability of our trains.
This state-of-the-art technology helps us identify deteriorating bearings up to three months prior to failure so that we can proactively remove affected railcars from service. Wayside scanner boxes with high-definition imaging conduct inspections of wear plates, springs and bolts for technology-driven train inspections in real time. CP’s predictive analysis catches critical wheel defects online, before they fail. Since implementing the model, CP has reduced in-transit bearing failures by 95 percent, significantly reducing the risk of derailments, infrastructure fatigue and service interruptions.
CP received an Outstanding Safety Leadership Award from the Railway Association of Canada in 2018 for this technology. For more information, see our Technology Driven Train Inspection video.
Read more in Asset and Rail Network Resiliency.