Optimizing robotic arm performance through state classification
This use case helps the robotic engineer at Apex Global gain a deeper understanding of robotic arm operations and identify areas for improvement. It aims to leverage data-driven insights to optimize robotic arm performance and improve production efficiency.
Customer success
Apex Global could increases production throughput by around 10% by optimizing robotic arm workflows based on operational state classification. It can reduce downtime, increase production efficiency, and lower maintenance costs by performing preventive maintenance only when needed. It could also improve safety by preventing catastrophic failures.
Scenario
Apex Global relies heavily on robotic arms for its production line. Unexpected downtime due to robotic arm malfunctions can lead to significant production losses. The company wants to implement a predictive maintenance strategy to identify potential issues before they cause failures.
Workflow
Task | Role | Action | Reference |
|---|---|---|---|
1 | Robotics Engineer | Add the following details:
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2 | Robotics Engineer | Configure the following parameters:
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3 | Robotics Engineer | Upload the ZIP file containing the pre-trained RandomForest model. | |
4 | Robotics Engineer | Optionally, configure events to trigger alerts when the arm enters an Error state. | |
5 | Robotics Engineer | Deploy the model to the relevant BMC Helix Edge nodes. |
Results
The system now monitors robotic arm data in real time and generates alerts when anomalies are detected, allowing maintenance teams to address potential issues proactively.