Case Study

Transforming Long Haulage Down Under

Road trains in Australia have to stand up to incredible wear and tear. Here’s how Intangles helps maintain vehicle health for the biggest of big rigs.

From monitoring and predicting maintenance issues to preventing fuel theft, Digital Twin Technology is already helping fleet managers control costs.

Trucks break down. Drivers make errors in judgment. Road and traffic conditions are always in flux. Those are challenges that fleet managers and vehicle operators routinely face, and navigating them successfully means making the right decision hundreds of times a day. Today, Digital Twins – even though the technology is still relatively new – are already helping them do that.

 

The real-world applications of Intangles’ proprietary Digital Twin and AI platform provide useful illustrations of the technology’s potential for fleet management. Incorporating telematics, advanced analytics and powerful algorithms, the company focuses on predictive maintenance, driver behavior analysis and operational efficiency, providing insights, real-time alerts and even repair strategies.

 

Vehicle health
With a Digital Twin of a truck, Intangles’ platform can identify potential failures and vehicle maintenance issues days or even weeks before a Diagnostic Trouble Code, or DTC, is generated. Leveraging historical and real-time data, the platform also gives insight into the severity of the potential problem (minor, major or critical), enabling more effective decision-making for addressing maintenance or repair needs. For Intangles customers, the result has consistently been a significant reduction in on-road vehicle breakdowns, creating savings in maintenance and repair costs, as well as unplanned towing. It also means that fleet operators must make restitution to their customers or pay for alternative delivery methods less often. Predictive algorithms generated with the Digital Twin enable preventive and proactive maintenance rather than waiting for a DTC or a vehicle breakdown – saving time, money and vital customer relationships at the same time.


A good example is the monitoring and modeling of air intake in Intangles’ Digital Twin platform (Figure NUMBER). The platform tracks engine turbocharger boost pressure in real time and is continuously “learning” from other similar twins to develop a mean boost pressure for the vehicle. Then, it compares the real-time data to the calculated mean, potentially identifying issues well before the truck’s onboard ECU generates an alert.

 

Since launching in 2016, the Intangles Digital Twin solution has detected millions of critical faults before a DTC was triggered; for Intangles-equipped fleet operators, that has resulted in a 75% reduction in breakdowns, a 10%-30% increase in asset availability, and a 5%-10% decrease in maintenance costs.

Case study: Municipal Fleet

A fleet of 1,400 municipal vehicles, including 90 refuse trucks from three different OEMs, were equipped with the Intangles AI-powered fleet maintenance system, with a view to improving turnaround times, fuel efficiency and preventative maintenance. During the pilot phase, the Intangles platform detected faults in 30% of the trucks – all before DTCs were triggered. The predictive alerts saved the municipality thousands of dollars in maintenance costs and helped reduce downtime, while increasing the fleet’s fuel efficiency by an estimated 5.6%. In total, the municipality realized cost efficiencies amounting to US$500 per vehicle per month.

Fuel monitoring
In the trucking industry, fuel is a precious and expensive commodity. So, too, is DEF, the catalytic converter additive that helps reduce exhaust emissions and is often in short supply. As a result, fuel and DEF theft has become a major concern for fleet managers and vehicle owners, and several fuel-tracking devices have come on market to address that concern. However, most of these products have been severely limited in their ability to provide detailed information about the location of potential theft, the amount of fuel/DEF missing, or the time of the suspected pilferage. 

 

Intangles’ Digital Twins can provide fleet operators with far more detailed and useful information. By leveraging sensor-derived and historical data into machine-learning algorithms, the Intangles Digital Twin can detect the exact time, location and number of fillings and potential thefts. (See Figure NUMBER.) The modeling can also help fleet operators compute an accurate cost of fuel consumed per kilometer. Fleet managers using the Intangles platform 

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