The future of maintenance is a predictive one

Sept. 28, 2016
Fleets that are able to read and interpret certain data about their vehicles will be able to anticipate asset failure before it happens, take effective action and enjoy a competitive advantage over their counterparts.

It’s no secret that reducing vehicle downtime is paramount to preserving a fleet’s bottom line. The importance of being able to quickly and effectively address problems that arise and get a truck back up, running and moving cannot be understated.

However, the ongoing and ever-improving development of technology is giving fleets the opportunity to go beyond improving uptime and reducing the harm done by unplanned repairs and service. Predictive maintenance is becoming a reality in the commercial vehicle industry, and fleets that are able to read and interpret certain data about their vehicles will be able to anticipate asset failure before it happens, take effective action and enjoy a competitive advantage over their counterparts.

“Systems can actually predict when a failure is going to occur,” says Brian McLaughlin, president, PeopleNet, a provider of fleet management solutions, including fleet mobility technology, tracking solutions and safety and compliance offerings (www.peoplenetonline.com).”We feel like we can do this today with historical lifecycle analysis, truck driving history and vehicle demographics. Put it all together and we can see the future fate of a truck.”

Fleets today are focused on compliance, safety expectations and the march toward the reality of autonomous vehicles. These factors are increasing the complexity of the equipment necessary to move freight. In turn, the ability to perform maintenance on said equipment is becoming more important than ever before. And there’s no better way to address a problem with a vehicle than before component fails or a breakdown occurs.

Data capture

“It’s all about capturing an actual experience, capturing the measurements being pulled off a vehicle, and then being able to apply knowledge against those other factors,” says Dave Wangler, president, TMW Systems, a developer of enterprise management software for the surface transportation services industry (www.tmwsystems.com).

According to Wangler, it was about a decade ago that a couple of very large carriers (fleets with approximately 10,000-20,000 assets) came to the realization that they had enough volume, equipment and experience to accurately predict when certain vehicle components were going to fail.

“Now we have 675 companies using TMT (fleet maintenance software), and we’re capturing information about every repair and every PM,” he continues. “We’re also capturing information and understanding the vehicle’s brand, make, model and year, and it is certainly possible to capture enough of that information together in a big data environment and be able to predict.”

The ultimate goal is to capture, evaluate and understand actual repair experiences and use the information gained to commence with predictive maintenance.

Patterns

According to PeopleNet's McLaughlin his company can secure hundreds of data elements off of a vehicle engine and utilize the information to learn industry patterns.

“All of the OEMs are racing to implement different strategies for remote diagnostics, and they are all heading down a path toward wanting to get into prognostics as well,” he continues. “It’s really in their best interest, from a warranty perspective and a customer service perspective. And they also want visibility in terms of how their engines and trucks are holding up.”

Obtaining access to and digesting critical vehicle information allows fleets to react in real time and evaluate the data against additional information provided by the manufacturer or an experience. Then predictive maintenance can occur. 

“This is a phenomenal opportunity for the community,” says Wangler. 

“He or she who has the most breadth and depth of data is going to win,” adds McLaughlin.

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