Predictive Maintenance Gains Importance as Factories Deploy More Robots

Robot data combined with predictive analytics can improve uptime and productivity, says Advanced Technology Services (ATS). Factories are adding more robots for repetitive, precise, and physically demanding work at high speed, making system availability increasingly important. The International Federation of Robotics reports about 5 million industrial robots in operation worldwide in 2025, up 11% from the prior year.

An unexpected robot failure can interrupt a cell, slow a line, and create delays that spread through the plant. As manufacturers rely more on robotic systems, identifying developing problems before they force equipment offline becomes valuable.

Traditional preventive maintenance follows a schedule based on time, usage, or manufacturer recommendations. That approach may not reveal what's happening inside a robot between planned service intervals. A joint, reducer, motor, or controller can show signs of wear while the robot is still operating.

Predictive maintenance uses equipment data to identify changes that may point to developing faults. For robotics, maintenance decisions can be based on actual equipment condition rather than a calendar alone.

Predictive maintenance can monitor various operating data from industrial automation systems. Useful signals depend on the robot, application, and failure mode. Common examples include vibration, temperature, and motor current; torque, controller alarms, cycle data, and maintenance history provide context.

The goal is to capture data that reveals changes in how a robot operates, not to collect every data point. A rise in vibration may indicate a mechanical issue, while changes in motor current or torque can signal added resistance or abnormal loading. Temperature variations may show a component isn't operating as expected.

Changes in an end effector, sensor, conveyor, or process can affect robot performance. Viewing the robot within its workcell helps separate normal variation from changes that warrant maintenance, especially when the robot handles different loads, speeds, or motion paths.

Reliability monitoring gathers machine data through sensors and connected systems, and analytics tools identify changes in equipment behavior. When a pattern changes, teams can investigate before a failure forces the robot offline.

Changes in equipment performance, combined with environmental or process data, can provide warning of problems, says ATI. Collecting data is only the first step; the information must support a practical maintenance decision. A useful alert helps a team determine what needs attention, how urgent the issue is, and when the robot can be inspected without causing unnecessary production interruption.

This becomes more important as factories operate larger robot fleets. Maintenance teams may not have time to inspect every asset at the same frequency. Predictive maintenance directs attention toward equipment showing signs of change while allowing healthy assets to remain in service.

Earlier warnings also improve parts planning. If a component shows signs of deterioration, teams may have more time to confirm the problem, locate the correct replacement part, and schedule work during a planned maintenance window.

Earlier warnings can reduce downtime and improve productivity, says ATS. The value of predictive maintenance goes beyond preventing unplanned stops. Finding issues early can limit secondary damage and help manufacturers get more useful life from robotic assets. Planned repairs also reduce the disruption that occurs when technicians must troubleshoot a failed robot during production.

A planned maintenance window gives teams more control over when equipment is taken offline and serviced. That supports safer maintenance practices because technicians can prepare for the work instead of responding to an unexpected failure under production pressure.

As more factories rely heavily on robotics, maintenance strategies must account for how systems perform throughout their service life. Predictive maintenance uses equipment data, condition signals, and analytics to identify developing issues earlier. When that information leads to timely inspection and repair, manufacturers can reduce unplanned downtime, protect robotic assets, and keep production schedules more stable.

Rob Carbone is vice president of operations at Advanced Technology Services, where he leads initiatives to help manufacturers improve equipment reliability, maintenance excellence, and customer success. This article originally appeared on The Robot Report.