Why predictive maintenance is more important when factories add robots
Robot data combined with predictive analytics can improve uptime and productivity, ATS says. Source: Pexels, Advanced Technology Services
Factories are adding more robots because they can handle repetitive, precise, and physically demanding jobs at high speed. As robotics takes on a larger share of manufacturing, keeping such systems available becomes more important.
The International Robotics Federation (IFR) reported approximately 5 million industrial robots in operation worldwide in 2025, up 11% from the previous year.
An unexpected robot failure can affect more than one machine. It can disrupt a cell, slow a line and create delays that spread through the plant. As manufacturers increasingly rely on robotic systems, the need to identify developing problems before they take equipment offline becomes increasingly valuable.
Why robotic systems raise the stakes
Traditional preventative maintenance follows a schedule based on time, usage, or manufacturer recommendations. This approach still has value, but may not reveal what is happening inside a robot between scheduled maintenance intervals. A joint, gearbox, motor or controller may start to show signs of wear while the robot is still running.
Predictive maintenance uses equipment data to identify changes that could indicate the development of faults. NIST describes condition monitoring such as the detection, diagnosis, or prediction of faults and failures in industrial equipment.
For robotics, this means that maintenance decisions can be based more on the actual condition of the equipment than on a simple calendar.
Propose your innovation now!
What predictive maintenance can monitor
Industrial automation and related systems generate many types of operational data. Useful signals depend on the robot, the application, and the failure mode. Common examples include vibration, temperature, and motor current. Torque, controller alarms, cycle data and maintenance history can also provide useful context.
The goal is not to collect all available data points. Teams need data that can reveal changes related to how a robot operates. Increased vibration may indicate a mechanical problem, while changes in motor current or torque may indicate increased resistance or abnormal loading. Temperature changes can also signal that a component is not functioning as expected.
Changes to an end effector, sensor, conveyor, or surrounding process can impact the performance of the robot. Looking at the robot in the context of a larger work cell can help teams separate normal operational variation resulting from a modification that may require maintenance attention. This distinction is especially important if the same robot handles different loads, speeds or motion paths.
This is where reliability monitoring can help. Sensors and connected systems can collect machine data, while monitoring and analytics tools help identify changes in equipment behavior. When a pattern changes, maintenance teams can then investigate the cause before a failure forces the robot offline.
Changes in equipment performance, as well as environmental or process data, can alert you to problems, ATI says. Source: Pexels, Advanced Technology Services
Transform robot data into maintenance decisions
Collecting data is just the first step. The information must support a practical maintenance decision. A helpful alert should help the team determine what needs attention, how urgent the problem is, and when the robot can be inspected without causing an unnecessary interruption to production.
This becomes even more important as factories operate larger fleets of robots. Maintenance teams may not have time to inspect every asset as frequently. Predictive maintenance can help direct attention to equipment that shows signs of change, while allowing healthy assets to remain in service.
Timely alerts can also improve part scheduling. If a component shows signs of deterioration, teams can have more time to confirm the problem, locate the correct replacement part and schedule work during a scheduled maintenance window.
Early alerts can reduce downtime and improve productivity, ATS says. Source: Pexels, Advanced Technology Services
Improve productivity, asset life and safety
TThe value of predictive maintenance goes beyond preventing unexpected shutdowns. Identifying problems early can limit secondary damage and help manufacturers get longer life from robotic assets. Planned repairs can also reduce the inconvenience that occurs when technicians must troubleshoot a broken robot during production.
A scheduled maintenance window gives teams more control over when equipment is taken offline and serviced. This can support safer maintenance practices because technicians can prepare for work instead of responding to an unexpected failure under the pressure of production.
As more factories rely heavily on robotics, maintenance strategies must take into account the performance of such systems throughout their service life. Predictive maintenance gives manufacturers the ability to use equipment data, condition signals and analytics to identify developing problems early. When such information leads to timely inspections and repairs, manufacturers can reduce unplanned downtime, protect robotic assets, and keep production schedules more stable.
About the author
Rob Carbone is vice president of operations at Advanced Technology Services. He is responsible for leading initiatives that help manufacturers improve equipment reliability, maintenance excellence and customer success.
Post Comment