Locomotive Maintenance supported by Assisted Reality

In 2020, the eyllo team, under my leadership, embarked on a Proof of Concept (PoC) by utilizing our smart-glasses software solution to assist a logistics company in the inspection and maintenance of their locomotives. This project aimed to enhance the efficiency of technicians and improve key performance indicators in the vast railway network of Brazil.

Context

The logistics company operates a comprehensive railway network, connecting the central region of Brazil with states in the south and southeast. Locomotive maintenance involves multiple disciplines, with the main maintenance center located in the south region. In this maintenance center, technicians are able to perform all necessary tasks to keep the locomotives operational. Technicians stationed along the routes handle routine tasks, and specialized technicians are dispatched when needed.

For this project, among the key performance indicators used to measure maintenance quality, we focused on two relevant ones:

• MTTR (Mean Time To Repair), that is, the mean time it takes to fix problems once the service center receives a notification of a failure.
• MKBF (Mean Kilometers Between Failure), that is, the mean distance a locomotive is able to travel, operating properly and generating revenue, before it fails and requires assistance.

Proof of Concept

RealWear smart-glasses, integrated with our proprietary software eyllo Field Service, were deployed to streamline workflow execution for service orders and remote expert assistance. To reduce MTTR, in locations where connectivity was available, the field technician was able to make an audio/video call with a remote expert. The remote expert was able to see the situation of the locomotive, have a better understanding of the issue and promptly assist the field technician. For improving MKBF, the focus was on reducing task variability. Digitizing workflows with step-by-step instructions reduced variability, ensuring consistent and high-quality outcomes.

Experiments

We designed and ran two experiments to assess the proper use of our solution to help reduce time to repair and task variability.

1. Remote Expert Assistance: The first experiment involved using a remote expert located at the main service center to assist a technician in the field. The expert at the service center used a regular web browser on a personal computer. The field technician used our mobile application installed on the smart-glasses. The smart-glassed were connected via wi-fi to a cellphone working as an access point.
2. Workflow Digitization: For the second experiment, we created step-by-step instructions to assist with the service orders. Together with the customer, we selected two workflows. We created the content with step-by-step instructions and uploaded the content to our solution. The technician would scan the QR code and follow the visual and audio instructions in order to properly execute the tasks.

Results

In collaboration with the customer, the PoC successfully deployed an Assisted Reality solution, significantly reducing task variability and minimizing the time required to repair locomotives in distant locations from the main service center. The use of smart-glasses and step-by-step instructions demonstrated tangible improvements in maintenance efficiency and overall operational quality.

This PoC not only marks a significant milestone in locomotive maintenance but also showcases the potential of Assisted Reality solutions in addressing unique challenges within the transportation industry. As we continue to innovate and refine our solutions, we anticipate further advancements in optimizing maintenance processes across diverse sectors.