AI-Powered Solutions for the Railroad Industry

Automation in the Rail Yard

With Computer Vision and Machine Learning, many tasks can be automated, reducing human effort

See Use Cases

AI – Powered Track Inspections

Utilize computer vision algorithms to identify the presence (or absence) of mandatory signage

See it in action

Predicted Fuel Burn with Machine Learning

Our Machine Learning models can be used to identify insights from other onboard system data, such as sensor data to predict fuel burn

Learn about Predictive Analytics

Schedule your demo today!

Demo

Railroad Industry Solutions

With NLP Logix, Artificial Intelligence plays a leading role in modern railroading by using applications like Computer Vision, Machine Learning, and Predictive Analytics to improve reliability and performance while reducing human effort.

Automation to Reduce Human Effort

Utilizing NLP Logix’s solutions, train maintenance can become semi-automated.

Improper cotter pin connections can be detected using lidar images taken with a drone flying over the hump yard, saving many man hours over manually checking connections.

Track cameras can be utilized to identify missing tank caps, engaged brake pistons, damaged rail cars, and more.

What could you accomplish with Computer Vision and Machine Learning making your tasks easier?

Let’s Talk

Track Inspections with Computer Vision

Using forward facing cameras on a locomotive, video footage of a track can be captured and analyzed. NLP Logix’s AI-powered solution can then utilize computer vision algorithms to identify the presence (or absence) of mandatory signage in known locations. This process allows for daily review of all track miles, and only requires human intervention when a potential missing sign is identified.

A similar model can be constructed for other track maintenance issues that can be visually identified.

Computer vision detecting properly/improperly placed signage

Predictive Analytics

Machine Learning models can be used to identify insights from onboard system data, such as sensor data.

One such application involves determining the predicted fuel burn based on the condition of the train, the track, the forecasted weather, any current maintenance issues, the train’s load, and factoring in other trains in the area. These ML models can be used for prediction alone or in combination with advanced analytics to allow further analysis.

Is it time to put Artificial Intelligence to work for you?

We Can Help