Driving Improved Asset Reliability and Operator Safety with Predictive AI

Utility fleets are tough on equipment, sometimes in ways that can be difficult to see. A bucket truck running uneven loads on unpaved roads during storm restoration efforts, for example, will not experience wear and tear in the same way as a delivery van on a highway route. Patterns that lead to failures tend to be gradual and distributed across multiple systems.

In heavy-duty use, traditional mileage- or time-based maintenance schedules can sideline trucks due to unexpected failures. Artificial intelligence-driven predictive maintenance platforms – which go beyond engine diagnostics to forecast tire failures, body wear and even driver behavior patterns before they become safety or cost events – are designed to help utility fleets address this issue.

Anticipating Failures
The industry is moving toward condition-based maintenance. Rather than service equipment on a fixed schedule, some fleets now perform maintenance based on what the asset’s data says about its true condition. This capability is enabled by platforms that link engine health, utilization, GPS and sensor data across the fleet.

Currently available AI-driven platforms integrate vehicle telematics, engine diagnostics, dash cams and equipment monitoring into a single system. Subscribers (i.e., fleet managers) receive fault code alerts, automated preventive maintenance scheduling notifications and digital vehicle inspection reports, helping them to identify developing problems.

Tire-related issues remain a significant contributor to unscheduled downtime, often causing fleets cascading delays. Utilities can mitigate this by investing in automated yard-entry scanning solutions that inspect a vehicle’s tires the moment the vehicle returns to the terminal. With integrated systems, collected tire data then flows directly into the repair order system.

“By identifying immediate threats – such as a foreign object or nail – while the vehicle is still in the yard, fleets can address the repair instantly, preventing a far more expensive and dangerous roadside failure on the next trip,” said Brian Mulshine, senior director of product management for Trimble (https://transportation.trimble.com).

Driver Safety
Driver safety is another area ripe for predictive AI improvements, with high-stress storm recovery events increasingly being targeted by safety and fleet managers.

“These grueling shifts put crews at significant risk, especially during the exhausted drive home after a long day of restoration work,” Mulshine said.

To measure operator alertness in real time, some utility fleets have begun piloting wearable sensors. Becoming even more common is the adoption of AI-powered driver-facing cameras, designed to instantly alert both operators and management to signs of microsleep and distracted driving.

New Insights
Continued AI platform advancements are expected to connect components missed by traditional diagnostics. How? By embracing assets with mixed duty cycles, operating in off-road conditions and equipped with specialized equipment (e.g., buckets, digger derricks), which create wear patterns that standard engine data won’t catch.

Per Nihar Gupta, vice president of product at Motive (https://gomotive.com), “Platforms are evolving to connect engine hours, PTO and boom time, utilization and job type to how each asset is actually used in the field.”

As utilities implement monitoring solutions, providers like Motive and Trimble can use that data foundation to build predictive models that go beyond what a fleet could develop on its own. Over time, the data could enable predictions for high-failure systems like brakes, diesel particulate filters and aftertreatment, with maintenance recommendations driven by actual operating conditions rather than static schedules.

Today’s connected vehicles generate a flood of data that can be analyzed for deeper insights. For example, exhaust back-pressure data could reveal filter or engine issues via pressure fluctuations. Drive-train problems could be identified through engine load and transmission temperature trends. Fuel consumption anomalies, when correlated with route and load data, may indicate mechanical drag or efficiency loss.

“These aren’t exotic data sources,” Gupta said. “The challenge is bringing them together and knowing what to look for.”

But that flood of vehicle data creates its own set of issues. While engines produce structured, standardized diagnostic data, components like tires and hydraulics do not. And body and chassis wear is still largely captured through manual inspection.

For utility fleets with mixed asset types spread across expansive service territories, it is genuinely difficult to build a coherent picture of asset health from fragmented data.

“Bringing those disparate signals into a platform where AI can use them requires standardized integrations and a flexible data architecture,” Gupta said.

Eliminating Barriers
The real hurdle for modern utility organizations is making their fleet management system data readily accessible to strategic AI partners, who can then analyze full asset histories to generate superior predictive insights.

“Eliminating data silos provides fleet managers with the flexibility to choose the specialized AI solutions that best fit their unique operations while ensuring those insights integrate seamlessly back into their core fleet management workflow,” Mulshine said.

About the Author: Gary L. Wollenhaupt is a Colorado Springs-based freelance writer who covers the transportation, energy and technology sectors for a variety of publications and companies.

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Sidebar: Wired Wheels
To connect existing vehicles to fleet maintenance platforms, telematics devices are coming online for tires and even lug nuts.

Michelin (www.michelinman.com) has launched smart predictive tire monitoring for Class 7 and 8 fleets. The goal is to proactively manage tire health, reduce costs and improve uptime using real-time pressure and temperature data and predictive insights. Michelin’s system detects early signs of tire degradation and sends alerts before failure strikes. Early testing showed up to 80% fewer emergency roadside tire events and up to 9% longer tire life when underinflation was corrected (see https://michelinmedia.com/pages/blog/detail/article/c/a1452/).

The WheelShield lug nut monitoring system from SensorMoto (https://sensormoto.com) provides a critical early warning when an asset’s wheel begins to loosen. Traditional maintenance schedules and manual checks could leave a fleet exposed to problems between service intervals. Direct monitoring responds to the first indication of a loose lug nut, well before a wheel-off incident could occur.