Class I railroad experiences improved rail safety by predicting bearing failures
For many railroads, overheated axle boxes and brakes are a major cause of derailments. Moving towards increased safety means that early detection through wayside monitoring equipment is critical.
Our customer is a Class I railroad in North America with a rail network of approximately 6,600 route miles, 13,000 freight cars, and 1,044 locomotives. They wanted to obtain a maintenance crew to fix a hot box detector before it could malfunction and stop trains running on the tracks. With massive data being generated and facing challenges interpreting the data to respond to the hotbox detectors in time, they partnered with CloudMoyo.
In this case study, you’ll find out how CloudMoyo enabled the railroad to ingest large volumes of streaming data from hotbox detectors into an Azure Data Lake, set up a data warehouse for data validation, set up data governance processes, and create a future-proofing solution using the Azure Data Platform.
Some of the benefits the solution drove for the railroad include:
- Reduced frequency of manual inspections
- Greater reliability of the system to prevent unnecessary delays
- Unnecessary delays caused by false alarms
- Ensured increased safety
Download your copy of the case study to discover these and more benefits that are driving safety in this railroad.
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CloudMoyo empowers rail and transportation companies to gain greater insights, unlock efficiencies, and improve agility in operations, revenue and asset management as well as critical areas of safety, crew scheduling and maintenance. Combined with deep rail industry expertise, our cloud-based, AI-driven solutions leverage cutting edge technology to reimagine railroad digitization.
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