Most predictive maintenance projects in robotics stall at the same point. The sensors are installed, the models are trained, the dashboard turns amber, and then nothing happens.
An anomaly score is not a decision. Somebody still has to allocate a technician, confirm the spare reducer is on the shelf and find a slot where the cell can stand still for four hours.
That gap between detection and execution is where the return on a monitoring programme is either realised or lost. For a single robot it can be bridged by a diligent maintenance lead with a spreadsheet. For a fleet of two hundred arms across six lines, it cannot. [Read more…] about Predictive Maintenance for Robot Fleets, from Condition Data to Work Order









