Utilizing Machine Learning for Predictive Maintenance

Predictive maintenance originated from the concept of condition-based maintenance, a strategy that revolves around monitoring the condition of a piece of equipment to decide what maintenance is needed based on indicators of decreasing performance or pending failure.

Building on this concept, a predictive maintenance system utilizes real-time data, historical fault logs and AI to monitor the condition of equipment to predict routine maintenance and alert operators to lagging performance.

As an industry, reliable equipment is essential. Utilizing a predictive maintenance system (PMS) helps with efficiency, lowering repair costs and reducing unexpected breakdowns for drivers on the road. In doing so, it also improves driver safety by minimizing the likelihood of equipment failure or an on-the-road event.

Building a Predictive Maintenance System That Works

Having a strong PMS can help reduce friction in the system by monitoring the health and lifecycle of different parts. It moves truck maintenance from unplanned to planned and helps with optimizing asset utilization.

Werner’s predictive maintenance system uses data from more than 100 onboard truck sensors and IoT devices to analyze the condition of each vehicle using API integrations and ML to predict when maintenance is required—eliminating costly, disruptive and unexpected repairs.

Take tires for example. Our PMS can predict when they’ll need to be replaced due to low treads or alert when tire pressure is getting low. In this case, the artificial intelligence of the ML analyzes the data coming in from the onboard sensors and then flags the maintenance recommendation for both the driver and our maintenance team. In turn, the driver is able to plan for the replacement in between routes and avoid a possible road incident that could have resulted in injury, damages and delays.

To Get Ahead of Maintenance, Technology is a Must

Fleets and drivers are at their best when they can be on the road. Reliable equipment plays a big part in driver and customer retention. If a company is not addressing the needs of their fleets in a timely manner, or supporting their drivers in every way possible, it’s going to have a negative impact on the company.

By embracing the latest in machine learning and artificial intelligence, Werner continues to develop and expand our PMS. Our goal is to replace reactive maintenance practices with a ML-based scheduled maintenance program. Not only will this make our trucks more efficient, but it will also improve the safety of our drivers and reduce delays for our alliance carriers and shipping partners. All thanks to technology.