Imagine if you knew when your truck would breakdown before it happened? How much would this improve your business’ uptime and productivity? With the help of data, our understanding of how vehicles work is improving fast. And with the help of artificial intelligence (AI) and machine learning, it will become possible to predict breakdowns with even greater accuracy.
Traditionally, the main approach to maximising uptime has been regular, scheduled servicing and reactive measures like break-down support services. But with the range of sensors and wireless technologies typically found on today’s trucks, businesses can be far more proactive.
The core of connected services and preventative maintenance is that by using wireless technology and sensors, it is now possible to collect vast amounts of data from a vehicle in real time. By analysing this data and identifying patterns, it is then possible to predict and anticipate a fault before it occurs. This gives you time to schedule a workshop visit at your convenience, and then fix the fault before it causes an unexpected breakdown.
“In the short time I have been working in this field, I have seen the technologies and our capabilities expand exponentially,” says Matthias Tytgat, manager of Volvo Trucks’ Monitoring Centre in Ghent, Belgium.
“In 2016, we were remotely monitoring just one component and it took us a full day to complete a full check in a fleet of several hundred trucks. Today, we’re monitoring multiple components in tens of thousands of trucks, and we can complete a full check of the whole fleet in just eight minutes. And the exciting part is that we’re constantly improving.”
The more data a system can analyse, the more accurately it can predict outcomes. Initially, connected services and real-time monitoring services had been designed to react to certain thresholds or sensor values for individual parameters as a means for predicting faults. For example, the engine exceeding a set temperature.
“While these sorts of insights are useful, it can be somewhat limited because it does not take into account the vehicle’s unique circumstances and driving conditions,” explains Matthias. “While it’s important to detect a potential fault as early as possible, it is also important not to bring a vehicle into the workshop unnecessarily either.”
Machine learning can be used to analyse greater volumes of data and to detect patterns impossible to define by a normal set of rules. This results in even more accurate predictions. Different parameters and data points from a wider variety of components and sensors can be combined, which is then analysed by AI systems to detect patterns indicative of potential problematic behaviour that is likely to lead to a breakdown.
For example, the temperatures of different parts can be analysed in combination with other factors such as vehicle mileage and fault codes. Once a machine learning algorithm has been trained to identify a pattern or combination of factors that often cause a particular fault, it then becomes possible to predict problems for individual vehicles no matter what type of application they’re in.
“It will be as if the service was created for a specific vehicle and its customer,” says Matthias. “And as we continue to improve our capacity to analyse data, the more accurate these systems will be.”
Lately there’s a lot of focus around data privacy and data security, and many drivers are uncomfortable with the prospect of being so closely monitored when working. These are valid concerns which is why it’s important that any connected service provider can offer the following:
To learn more about how connectivity and connected services can help truck owners improve their fleet operations, download our guide on technology and efficient driving. Here you will learn: