Extending robotic arm lifespan with remaining useful life prediction
This use case aims to proactively manage robotic arm motor maintenance and minimize unplanned downtime by predicting the motors' Remaining Useful Life (RUL). This allows scheduled maintenance interventions before failures occur, optimizing maintenance schedules, reducing costs associated with emergency repairs, and extending the equipment's operational lifespan.
Customer success
Apex Global could reduce robotic arm motor replacement costs by 18% by implementing predictive maintenance based on RUL predictions.
Scenario
Apex Global wants to minimize unplanned downtime due to robotic arm motor failures. To this end, it wants to predict the motors' remaining useful life (RUL) and schedule maintenance proactively. The company wants to reduce unplanned downtime and production losses, optimize maintenance scheduling and resource allocation, extend motor lifespan, and reduce replacement costs.
Workflow
Task | Role | Action | Reference |
|---|---|---|---|
1 | Maintenance Manager | To create a new ML model, do the following:
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2 | Maintenance Manager | To configure the ML model data, do the following:
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3 | Maintenance Manager | Upload the ZIP file containing the pre-trained regression model. | |
4 | Maintenance Manager | Optionally, configure events to trigger maintenance alerts when the predicted RUL falls below a certain threshold. | |
5 | Maintenance Manager | Deploy the model to the relevant BMC Helix Edge nodes. |
Results
The system provides predictions of motor RUL, enabling proactive maintenance scheduling and minimizing downtime.