ABB Launches New CBM Service for Fleet and Individual Robot Assessments

To optimize productivity and minimize downtime, global tech giant ABB has launched new Condition-Based Maintenance (CBM) service, enabling robot users to create a preventive maintenance schedule for individual or robot fleets based on real-time operational data.

January 22, 2021. By Manu Tayal

To optimize productivity and minimize downtime, global tech giant ABB has launched new Condition-Based Maintenance (CBM) service, enabling robot users to create a preventive maintenance schedule for individual or robot fleets based on real-time operational data.

Commenting on the development, Antti Matinlauri, Head of Product Management for ABB Robotics, said that “by providing greater predictability around maintenance and repair schedules, our condition-based maintenance service allows customers to get the most from their installed robots. Customers can now optimize their production efficiency by eliminating unexpected downtime caused by failures or delays in obtaining spare parts to fix a fault.”

“Users will also gain a better understanding of exactly which robots may have an increased risk of component failure, for example if they are over-utilized compared to others in a production line, or if heavy payloads are causing the robot to operate outside of its recommended design parameters for example,” Matinlauri added.

CBM uses real-time data on robot operations to help identify any potential issues that could affect performance, including duty, speed, acceleration, and gearbox wear. These variables are compared against other robots in ABB’s worldwide robot database to calculate the likelihood and timeframe of a potential fault or failure.

Aimed at customers with large fleets of robots, ABB’s CBM tool can advise whether remedial action is required, involving either repair or replacement of affected parts. By identifying which parts are likely to fail and when, spare parts can be purchased and prepared without having to hold them in stock, helping users to plan their budgets and ensure that resources are available to carry out the work when required.

Previously, it was difficult for users to determine whether key parts such as gearboxes were becoming worn or in need of replacement. This meant that problems were either undiagnosed until a failure, or parts were purchased unnecessarily or were un available when needed, disrupting production while the robot is offline.

The new CBM tool gives customers insights they need to create a preventive maintenance schedule based on known performance to help keep robots in good working order and to maximize performance. Monitoring also minimizes the likelihood of premature failure and extends the Mean Time Between Failure (MTBF) rate, as well as prolonging the operational life of the robot. 

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