Volvo Trucks has launched a new tire management service in Sweden as well as a major test pilot for advanced monitoring of trucks in Europe. By also applying machine learning, the accuracy of predicting and preventing unplanned stops will be improved even further.
“New technologies for monitoring and analyzing truck data in real time are opening up exciting opportunities to predict failures more precisely and further into the future. It’s all part of our continuous effort to keep our customers’ trucks on the road,” said Markus Efraimsson, VP uptime, Volvo Trucks.
The tire management service measures tire pressure and temperature in real time. Results can be monitored through an app, helping both the driver and owner to identify slow punctures and avoid possible tire explosions causing unplanned stops at high costs.
In addition, the new service also enables lower fuel consumption and more mileage to be gained from each tire through having the correct pressure and temperature. The tire management service will be rolled out successively in the European markets.
Volvo Trucks is also performing a test pilot including several other components for select customers with the company’s gold service contract. By monitoring and analyzing data from thousands of trucks in real time a large number of potential breakdowns have been avoided, resulting in improved uptime and productivity.
The aim is to predict component failures before they occur and provide the customers with optimal service planning. When a potential problem is detected by a Volvo Truck Monitoring Center, the customer’s local Volvo workshop is alerted so that preventive actions can be taken.
“We’re looking at uptime from a customer perspective. Our focus is really to secure that the customer has no unplanned stops,” said Efraimsson.
The next step is to gradually introduce machine learning. This form of artificial intelligence makes it possible to collect and analyze large amounts of truck data for the purposes of research and development. It allows Volvo Trucks to learn more and more about the health and performance of the truck, and hundreds of thousands of connected trucks, in everyday use.
By using advanced computer modeling and analytics, the ambition is to be able to identify hidden patterns to predict component failures far in advance, making it more likely that the required service or repair can be done during a scheduled service visit for maximum truck uptime.
