Gearbox.

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How Gearbox Issues Reveal Themselves in Motor Currents — and How Reliability AI Predicts Failures Months in Advance

Gearboxes are often at the heart of heavy industrial equipment, quietly transferring power between the motor and the driven machine. When issues develop inside a gearbox—whether it’s a misaligned shaft, a cracked tooth, or progressive bearing wear—the first signs aren’t always visible to the naked eye. Instead, they show up as vibrations.

Traditionally, vibration analysis using accelerometers placed on the gearbox or motor casing has been the primary way to detect these problems. But there’s a more subtle, earlier signal hidden in the electrical system itself—one that Reliability AI leverages to predict gearbox problems months before they become critical.

How Vibrations Travel from the Gearbox to the Motor and How to Predict Failures

When a gear tooth chips, or a bearing begins to pit, it creates a repetitive impact inside the gearbox. That impact sets off a chain reaction:

Mechanical vibration in the gearbox housing – The defect excites specific vibration frequencies that match gear mesh or bearing fault frequencies.
Propagation through the shaft – These vibrations travel along the shaft into the motor.
Electromagnetic disturbance in the motor – The rotor and stator magnetic fields interact with the shaft’s minute oscillations. This creates tiny fluctuations in the air gap between rotor and stator.
Electrical reflection in motor current – The disturbed magnetic field causes measurable ripples in the current and voltage powering the motor.

This means that gearbox faults don’t just shake the machine—they literally change the motor’s electrical signature.

How Reliability AI Detects These Subtle Changes

inside the Motor Control Center (MCC), voltage and current transformers (VTs and CTs) are already installed to monitor and protect the motor. Reliability AI taps into these existing sensors without requiring additional instrumentation.

By applying advanced algorithms, the platform monitors the magnetic field disturbances reflected in the current and voltage waveforms. Over time, it learns the healthy “baseline” of the machine and flags when new, repeatable disturbances begin to emerge.

Instead of waiting until vibration levels exceed alarm thresholds, Reliability AI detects the earliest fingerprints of mechanical degradation—often months in advance of conventional methods. Predicting gearbox failures in advance without mechanical monitoring can save hundreds of hours troubleshooting and field testing.

Why This Matters for Maintenance Teams

Early Warning – Maintenance can be scheduled at convenient times rather than reacting to emergency breakdowns.
No Extra Sensors Needed – Existing MCC infrastructure provides the raw data, lowering the barrier to adoption.
Broader Coverage – Motors in difficult or dangerous locations can still be monitored remotely without additional fieldwork.
Improved Safety & Uptime – Failures that once caused unexpected downtime can now be anticipated and prevented.

The Future of Reliability.

Gearboxes will always be critical components—and vulnerable ones. But with the ability to trace gearbox health through the motor’s electrical signature, plants can shift from reactive maintenance to predictive reliability strategies.

Reliability AI transforms what was once an invisible problem into a measurable, actionable signal—helping industries avoid costly downtime while extending the life of both motors and gearboxes.