Industrial Gearbox Condition Monitoring

Augury uses a multi-signal approach combining vibration analysis, temperature sensing, magnetic flux monitoring, and ultrasound detection to cover the full range of gearbox condition monitoring techniques across your asset base. Each method captures a different dimension of equipment health, so when one signal points to a developing fault, the others provide context and confirmation.

Before any alert reaches your maintenance team, certified CAT III and IV vibration analysts review AI-generated findings, adding an expert verification layer so every alert your team receives is worth acting on.

Learn more about Augury’s commitment to your predictive maintenance journey. 

Yes. With ultra-low RPM monitoring, your team can cover rotating equipment operating at as low as 1 RPM, including large industrial gearboxes in rotary kilns, ball mills, and extruders that standard monitoring systems struggle to reach.

At a leading metals and mining manufacturer, this capability helped prevent a potential production shutdown. Their rotary kilns operate at speeds where traditional vibration monitoring generates no useful signal, leaving the on-site team dependent on thermal imaging, which often detects damage only after it has progressed.

After deploying Augury’s Machine Health Ultra Low, sensors picked up unusual impacting patterns inside one of the kilns, pointing to refractory bricks beginning to come loose. Thermal imaging had missed the issue entirely. The maintenance team confirmed the finding, scheduled a controlled repair, and avoided a failure that would have taken days or weeks to recover from.

Gearbox condition monitoring detects a wide range of developing faults, including:

• Gear friction
• Shaft eccentricity
• Bearing failures within the gearbox
• Misalignment
• Lubrication deficiencies

Each alert comes with a root-cause diagnosis and severity assessment, so your maintenance team knows what the fault is, how serious it is, and when to act.

Yes. Our industrial AI calculates hundreds of unique parameters from each asset’s condition monitoring data, including frequency patterns, amplitude trends, and signal characteristics specific to bearing faults versus gear mesh defects.

Both fault types produce distinct signatures that the system distinguishes, even when both appear in the same asset simultaneously.

Our IoT sensors withstand the conditions found around heavy industrial gearboxes, including high ambient temperatures, water and chemical washdowns, dust, and continuous vibration. They meet IP65 and IP67 ratings and carry appropriate hazardous-area certifications for demanding production environments.

Your team can deploy gearbox condition monitoring without conduit runs or hard-wired connections. Sensors mount directly on the equipment housing and transmit data wirelessly to the cloud platform without adding installation complexity.

Yes, but the installation process doesn’t require production shutdowns or specialist contractors, and our team can complete it in a matter of hours. Your team starts receiving data and diagnostic insights the same day, and maintenance planning can shift from fixed inspection schedules to decisions driven by actual asset condition.

Discover the benefits of condition-based maintenance with our complete guide. 

A Forrester Total Economic Impact™ study* found that manufacturers using Augury’s solutions achieved a 310% return on investment in three years, recouping their initial investment in under six months.

Adopting predictive maintenance for gearboxes is what makes those numbers achievable: your team addresses faults weeks in advance, avoiding the cost of expedited parts and emergency repairs.

Beyond revenue protection, Augury customers also see extended gearbox service intervals, fewer machine failures, and maintenance labor redirected from reactive emergency work to planned, scheduled repairs.

*“The Total Economic Impact™ Of Augury Machine And Process Health” commissioned study conducted by Forrester Consulting on behalf of Augury, July 2025. Results are based on a composite organization representative of interviewed customers over three years.