Most fleets take a preventative approach to maintenance – pulling a truck off the road for scheduled service based on a predetermined number of miles or hours in operation. Unfortunately, when minor problems arise between service intervals what started as a minor issue can rapidly spiral into a more expensive repair that forces a vehicle out of service. The following are 3 reasons to shift to predictive maintenance programming.
Connectivity, machine learning, and artificial intelligence are revolutionizing maintenance practices for the trucking industry. With real-time telematics data feeding cloud-based algorithms, fleets can shift to a predictive maintenance program from the reactive or preventative plans that are commonplace today. This transition can be intimidating, but learning how to deploy predictive maintenance practices is worth the effort. Where to begin?
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1) Avoid big problems by fixing more minor issues earlier.
Unlike a reactive system, predictive maintenance shifts the focus to data-driven decisions. Instead of repairing a truck as something breaks or before a component’s end of life, trained algorithms can help spot weak links.
Maintenance teams can correct issues before they cause downtime and benefit from every bit of life from each vehicle component. Additionally, by coupling technology capable of automatically fixing issues such as automatic tire inflation (ATI), the number of problems requiring manual intervention can be decreased by up to 90%, engendering a high degree of user trust and engagement.
2) Trained algorithms give a more accurate picture of vehicle and component health.
Like the boy who cried wolf, a common complaint among fleets investing in new technologies is the number of false warnings with incomplete information. This misinformation causes drivers to tune out notifications, undercutting the system’s value.
While the early days of adding sensors to vehicles resulted in threshold-based alerting practices, trained algorithms can now identify issues nearly 70% sooner without frequent false positives.
By leveraging machine learning, programs like tire maintenance can leapfrog maintenance practices built on alerts from threshold-based tire pressure monitoring systems (TPMS) and manual maintenance practices. And with each mile, the insights generated from the system get smarter.
3) Plan downtime instead of coping with costly emergency downtime.
Machine learning can assess countless variables. Effective tire management includes elements such as geography, temperature, tire damage already accumulated, and pressure profile to diagnose and categorize issues by severity, which assists in cost-effective maintenance planning.
For example, by coupling active inflation with machine learning, fleets utilizing Aperia Technologies’ Halo Connect regularly trim unplanned tire-related downtime by 90%. The result is decreased on-road breakdowns, reduced technician diagnostic time, and increased automation of routine tasks.
These reasons enable fleet managers to confidently decide where and when to service vehicles, minimizing disruptions and maximizing equipment utilization. Some predictive maintenance systems for fleets are still maturing. However, ATIS technology and predictive analytics can transform fleet tire maintenance. Halo Connect, by Aperia Technologies, has been on the road for 50+ billion miles and is available now.
Tire failures are frequent and carry significant consequences. Tires also need to be checked and maintained more than any other component on a truck and therefore benefit most from the addition of automation paired with sensors and analytics. However, whether you begin with tires or another area of focus for your fleet, building a predictive maintenance foundation will enable more significant investments in predictive maintenance practices, translating to more savings and efficiency.
