Predictive Maintenance Module for Rail Maintenance

Using artificial intelligence and machine learning, the module transforms raw data into context-rich, actionable insights, enabling teams to detect, identify, alert and take action on operational anomalies.

Mushy Adobe Stock 512378391
Mushy AdobeStock_512378391

Shift5 launched the Shift5 Predictive Maintenance module, which provides real-time analysis of data from any onboard source to deliver actionable insights needed to predict and schedule maintenance effectively.

“Defense, aviation and rail maintenance and operations teams have demanding jobs compounded by high stakes: downtime limits DoD mission capable rates, impacts the flow of goods along the rail supply chain, and diminishes the reliability of thousands of daily global flights. Reactive maintenance capabilities are insufficient. Shift5's Predictive Maintenance Module flips the paradigm and allows teams to use real-time data to shift to a proactive PM stance,” says Shift5’s CTO, Egon Rinderer. “With our predictive maintenance Module, the Shift5 Platform becomes a comprehensive solution that improves the safety and reliability of fleets. We bring significant value to organizations with razor-thin margins for error in cybersecurity and operations.”

 

From Shift5:  

  • According to a survey of operators and maintainers in each space, two-thirds (66%) experienced preventable fleet downtime due to a lack of effective predictive maintenance. Furthermore, 77% agreed that their organization’s current tooling failed to provide the access and visibility needed for effective predictive maintenance.  
  • Shift5’s platform is hardware, bus, and protocol agnostic and performs passive, full-take data capture from any onboard source — every frame, every bus, every protocol. The platform analyzes data in real-time to provide owners, operators, and maintainers with data-driven insights needed to predict and schedule maintenance effectively. 
  • Using artificial intelligence and machine learning, the module transforms raw data into context-rich, actionable insights, enabling teams to detect, identify, alert and take action on operational anomalies.               
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