As a consultant and industry advisor to Auburn University's Transportation Institute, I love it when a major trucking fleet takes out a full page ad in an industry publication which, in my mind, asked a group of Auburn students and me for help. Here is what the ad says:
We're making it impossible to break down in the middle of nowhere
With remote diagnostics, we not only know if there's a problem with your engine, we know from 800 miles away. By monitoring vehicle data, we can help you make reliable, informed decisions, getting you back on the road quickly – and keeping you there. It's how we deliver confidence.
What caught my attention was the specific reference to the engine. This, of course, is a great improvement over the past. But, of all the headaches with trucks the engine is near the bottom of the list. Remote diagnostics on engines is great, but remote diagnostics on everything that could fail – enabled with artificial intelligence (AI) – would be better.
Tires and wheels are near the top of the problem list for commercial vehicles. I happen to know for a fact a group of Auburn University engineering students are working on getting wheel end data that can be monitored by an AI and Machine Learning (ML) system. An informed decision to stop the vehicle before a tire becomes a road gator (or the entire wheel and tire leave the vehicle) can be made by monitoring heat and vibration at the wheel end. This team is working on a shoe string budget. They would have made the Skunk Works proud.
Using the Tangerine Innovation AI and ML system, senior design students are monitoring a set of shock absorbers. AI will send an alert when one shock is not the same temperature as the others. If one shock doesn't heat up like the other three, for example, AI would report a leaking shock. What if out of the four shocks, one is getting extremely hot compared to the other three? That tiny bit of data might be an indication of loose lug nuts or a separating tire. Machine Learning is a great system for monitoring and comparing components and predicting failures before they occur.
The team is also looking into vibration monitoring as a backup to monitoring heat changes of the shock.
Jumping back to the engine: Most engine problems are cooling system related. A key component of the cooling system is the fan clutch, and it's amazing how much data can be collected to make informed decisions by monitoring the air supply that activates the fan clutch. It's a well-known that low air pressure supplying the fan clutch is a fan clutch killer.
Another team of engineers who are in their senior year at Auburn were successful in using the Tangerine Innovation AI and ML system to monitor the fan clutch of an Auburn National Center for Asphalt Technology (NCAT) tractor. This was after hours of bench testing on a fan clutch provided by Kit Masters and an AI system provided by Tangerine Innovation. The AI system could detect a fan clutch in distress and send a message that failure was imminent.
Auburn now has a patent pending on using AI to monitor the air-operated fan clutch on heavy duty commercial vehicles.
Other engine-adjacent components that can be monitored by vibration/sound inputs include the turbocharger, air compressor and the alternator, just to list a few.
I read there was going to be money in the Build Back Better plan for university research and development. That is a great start, but the real long term solution is for the trucking industry to support university research and development – if you really want a truck that won’t breakdown 800 miles from nowhere.
As a retired Technology & Maintenance Council Silver Spark Plug I feel I have done my part. What about you?
Bob Rutherford is a 50-year veteran of the trucking industry. Thirty of those years were as a member of the TMC where he earned both the Silver Spark Plug and Recognized Associate awards for his contributions to the industry. He currently is an industry advisor to Auburn University’s Transportation Institute working with student engineers on tomorrow’s solutions.