After successful deployments across South America and Africa, Razor Labs is extending its DataMind AI for mobile fleet to the Australian market.
DataMind AI is designed to deliver capabilities beyond original equipment manufacturer (OEM) and existing fleet health systems, detecting failures weeks earlier, tracing root causes with multi-sensor fusion and exposing operator-driven wear that other platforms can miss.
This translates into fewer breakdowns, longer component life, lower maintenance costs, and safer, reliability-driven operations, now proven worldwide and available for Australian fleets.
Early fault detection is so critical because intervention at an early stage prevents component damage and extends useful life.
In most systems, alarms are triggered only once sensor values cross fixed thresholds. Due to variable operating conditions and noisy signals, these thresholds are set high, so alarms often come after significant damage has occurred.
DataMind AI applies machine-learning models that compensate for operational variations and filter out noise, isolating true deterioration trends and detecting issues two to four weeks earlier,” Razor Labs said.
“Coverage includes 30-plus early detection modes across engines, transmissions, torque converters, brakes, hydraulics, suspensions and differentials.
“For example, on a CAT haul truck, DataMind AI detected abnormal lube and boost pressure three weeks before the OEM alarm. The site avoided 15 hours of downtime and protected 2400 tonnes of production, resulting in more than $400,000 of savings.”
From symptom to root cause
With so many alarms on a mine site, manual investigation is often required and true root causes of issues can be missed, leading to recurring failures.
Automated root cause diagnosis , however, helps to focus teams on the exact actions needed to fix the issue and prevent recurrence, cutting downtime and maintenance costs.
“DataMind AI does this by fusing data from truck sensors, fluid analysis, maintenance records, and even tire information” Razor Labs said. “In one example, DataMind AI flagged anomalies in air filter, crankcase pressure and oil filter sensors, but pinpointed the real cause: a deteriorated wiring harness on the shared 5V–ground line.
“The system recommended harness repair, which the team confirmed, preventing further failures that would otherwise reoccur.”
The way an operator drives a vehicle can also have a major impact on component lifespan.
DataMind AI is designed to identify dozens of types of operator behaviours that accelerate wear, enabling targeted and frequent training sessions with operators, focusing on each driver’s specific driving patterns, extending component life and reducing fleet maintenance costs.
“By identifying operator-induced wear, DataMind AI gives sites both the evidence to retrain operators and the insights to extend component lifespan,” Razor Labs said.
At the core of DataMind AI is a cloud platform purpose-built for mining, fusing multiple critical data sources: real-time on-board sensors; fluid analysis, including oil, coolant and fuel lab reports; maintenance data cross-referenced with real-time health monitoring to confirm whether maintenance actions were effective; tyre tread depth; pressures and temperatures; and dispatch data capturing operators, cycles and route conditions.
To enable seamless data collection, Razor Labs developed a mobile fleet data logger, a rugged device that connects to a vehicle’s CAN-bus or CDL, records the signals, and uploads them to the cloud whenever connectivity is available.
“The data logger ensures full fleet coverage across mixed OEM fleets and under extreme operating conditions,” Razor Labs said.
DataMind AI is designed to deliver capabilities that go far beyond existing systems.
This feature appeared in the October 2025 issue of Australian Mining.
