School of Graduate Studies

School of Graduate Studies
School of Graduate Studies

Only he can prevent haul truck fires – Meet Shelby Nicholson

Student in the Department of Mining     

by Phil Gaudreau, April 2020

Amy Cleaver

Imagine you’re working at a mine site on a haul truck that is three storeys high and weighing as much as 600 tons. It’s a scorching hot day and you’re hauling highly flammable materials. All of a sudden, you smell smoke. You don’t know whether it might be the engine, the tires, one of the many different fluids keeping the vehicle running, or something else.

It’s too high to safely jump, and you don’t know how long you have before the fire potentially threatens your life. What do you do?

Shelby Nicholson, Sc’17, a masters student in mining engineering, is hoping to help mining companies predict and avoid these sorts of incidents and other similar dangerous situations using the power of big data.

But first, a little history. Shelby worked as an automotive mechanic before going back to college to upgrade his electronics engineering skills. While working at Ontario Tech University’s wind tunnel, he became interested in completing a university degree and pursuing research.

Shelby was accepted into Queen’s engineering and offered a full scholarship through the Science ’48 ½ bursary for mature students. He decided to concentrate his studies on mining as it offered a little bit of every type of engineering and the industry had ample funding for research and projects.

As part of his studies, Shelby had the opportunity to operate a haul truck in Fort McMurray. It was there he became aware of some of the hazards of these large vehicles.

“These trucks are operating continuously, and the drivers are often working long shifts,” he explains. “This can lead to engine fires, tire fires, or accidents caused by exhausted drivers. This is bad for the truck, bad for the operator, and bad for those working alongside.”

So, with his appetite for research whetted by his time at Ontario Tech, Shelby decided to enrol in masters studies at Queen’s to try and develop a program which would help predict dangers on the job site. His aim is to develop an algorithm that would factor in how long a truck had been operating; its make, model, and age; the outdoor temperature; the length of a driver’s shift and his or her behaviour behind the wheel; and other important factors to offer site managers a heads up when a fire is likely to occur.

“From my automotive background, I know that these vehicles offer a lot of signals which could be helpful in predicting serious incidents,” he says. “I want to take that existing data and compile it into a useful format.”

As part of his research, Shelby spent a week in Arizona alongside his corporate partner Modular Mining Systems test driving a few haul trucks. This included both expected driving behaviours and what he calls ‘anomalous driving’ such as slowly drifting out of the lane followed by a jerking motion back into the lane–as if the driver was falling asleep behind the wheel.

Once his masters is complete, Shelby aims to start his PhD at Queen’s, gathering data from mines of truck fire incidents and safe truck operation to help provide a greater understanding of the causes of truck fires. He believes this sort of algorithm could later be adapted to other industries, such as mills.

“I enjoy graduate studies because, unlike undergraduate studies, you’re only studying what you’re interested in, so it’s a bit easier,” he says. “There are also lots of funding opportunities and awards if you are willing to go through the paperwork.”

Shelby was fortunate enough to have lived in Kingston before enrolling at Queen’s, and his wife has family in town, making the transition easier. The couple enjoys the size of Kingston, the cultural amenities, and the low traffic.

To learn more about the program, visit the Robert M. Buchanan School of Mining.