DATA-DRIVEN ONLINE MONITORING OF ROLLER BEARINGS IN WIND TURBINES

As highly-stressed components, roller bearings in the drive train of wind turbines are subject to countless influences from “manufacture, assembly and operation.” Bearing-specific changes in state can be detected at a relatively early stage through structure-borne soundbased traditional methods of roller bearing monitoring and diagnostics.

In connection with the monitoring of larger fleets, data-driven procedures help to prioritize irregularities by extracting characteristics from measured overall readings and diagnostic characteristics and assigning them to defined roller bearing conditions based on standards and guidelines. The prerequisite for these type of applications, however, are powerful and continuously measuring CMs, which work as edge devices and support IIOT-relevant protocols, such as MQTT.