Condition Based Maintenance
Optimize preventive maintenance plans with expert advice.
ABB's Condition-Based Maintenance (CBM) is a service designed to enhance the predictability of maintenance tasks and regular inspections, allowing for the maximum utilization of installed robots. By eliminating unexpected downtime caused by failures or delays in spare parts delivery during breakdowns, it optimizes production efficiency. The analysis results are presented in a color-coded list (histogram) that clearly represents the overall condition of the robot based on the status of each axis and the calculated axis usage scores. The formal report provided to customers includes an overview of the robot, a list of conditions, data analysis, individual maintenance recommendations, conclusions, and system evaluations.
basic information
Early detection of potential issues, whether from a single robot or multiple robots. Detailed analysis and recommendations regarding robot performance: Classification of robots based on duty factor (load time ratio) Overview of robots List of statuses Data analysis Maintenance recommendations for each robot Conclusion Evaluation CBM helps analyze the operational cycles of each ABB robot and detect robots under high load within the production line. Using statistical data collected from thousands of ABB robots worldwide over the past 20 years, evaluations are conducted based on specific criteria. The results of this evaluation are used to compare the performance of your robots with the global average. In-depth analysis of each robot leads to the creation of more detailed reports based on statistical data, which include customized preventive maintenance recommendations from ABB engineers. Prioritization of maintenance plans Customize and prioritize maintenance plans according to the usage and importance of the robots.
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Applications/Examples of results
- Present an overview of how robots are used in our company's operations and optimize future investments. - Provide detailed information on the most "burdened" robots and identify root causes. - Avoid the risk of unexpected gearbox failures during production and propose appropriate preventive maintenance strategies for the most "burdened" robots. - Support the budgeting and optimization of spare parts (forecasting future parts replacements). - Support the update strategy for new versions of robots (e.g., upgrading from IRB6640 to IRB6700).
