A service that creates a preventive maintenance schedule for individual robots or robot fleets to minimise downtime has been developed by ABB
The new condition-based maintenance (CBM) solution utilises real-time operational data to optimise productivity.
According to ABB, the tool can help identify any potential issues that could affect performance, including duty, speed, acceleration, and gearbox wear. It added these variables are compared against other robots in its worldwide robot database to calculate the likelihood and timeframe of a potential fault or failure.
CBM has been designed for ABB customers with large fleet of robots and 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, ABB said spare parts can be purchased and prepared without having to hold them in stock. It added that this helps users to plan budgets and ensure that resources are available to carry out the work when required.
Antti Matinlauri, head of product management for ABB Robotics, said: “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 optimise 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-utilised 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.”
ABB said it was previously difficult for users to determine whether key parts such as gearboxes were becoming worn or in need of replacement, which caused problems to go undiagnosed until a failure.
According to the company, CBM will not only resolve this but also extend the Mean Time Between Failure (MTBF) rate, as well as prolong the operational life of the robot.