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According to ABB, Zurich, Switzerland, its new Condition-Based Maintenance (CBM) service lets robot users create preventive-maintenance (PM) schedules for fleet and individual robots based on real-time operational data and, thus, optimize productivity and minimize downtime.

ABB’s CBM tool 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, the 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 unavailable when needed, disrupting production while the robot is offline.

To help customers decide which preventive measures to take, the CBM tool provides a report for each robot in question, 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.

ABB notes that the CBM offering is the latest in a suite of services from the company to help end-users get more from their robots. These comprehensive ABB services cover a range of areas, from installation and commissioning, repairs and replacements, spares and consumables, to robot-care packages.

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