ABB’s Condition-Based Maintainance (CBM) service enables robot users to create a preventive maintenance schedule for individual or robot fleets based on real-time operational data, to optimize productivity and minimize downtime.
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.
The CBM tool gives customers the 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.
To help customers decide which preventive measures to take, a report is provided for the robot, including its serial number, summary table, data analysis, individual maintenance recommendations, conclusions, and rating of the system. Using this data, the customer can then design an appropriate maintenance schedule, with help available from ABB if required.
