Cover story

PREDICTING THE OPERATING LIFE OF BEARINGS THROUGH THE COLLECTION AND EVALUATION OF ACTUAL OPERATING DATA

Actual operating data offers immense potential for improving wind turbine drive trains’ rolling bearing supports, optimising their operation. Collecting, evaluating and interpreting data makes it possible to more accurately define safety factors and adapt them for new developments. Schaeffler is developing, on its own or in collaboration with clients, new concepts to register variables that can cause damages to bearings. The aim is to detect unfavourable operating conditions and, using an adapted maintenance and operating strategy, prevent these or initiate countermeasures at an early stage.

New solutions for predicting the operating life of wind turbine gearbox components

ZF and Schaeffler are collaborating to develop new solutions for predicting the operating life of wind turbine gearbox components based on the actual loads that occur during operation. The first wind turbine gearboxes equipped with sensors and condition monitoring systems have been supplying operating data to a cloud-to-cloud solution.

The idea behind this project is to utilise ZF’s software solution for wind turbine gearboxes as part of a smart system, to provide wind farm operators with an aggregated overview of each gearbox. Schaeffler is a preferred partner for rolling bearings and supplies rolling bearing load analyses, while ZF itself assesses the loads placed on the gearbox components.

Pre-processed data from the condition monitoring system and other sensors is transmitted to the ZF cloud, while torque and speed data is forwarded to the Schaeffler cloud, where a detailed simulation model of the ZF gearbox has been implemented as a virtual twin.

The calculation results from the virtual twin are transmitted back to the ZF cloud and are then available on the ZF software’s dashboard for use in monitoring the gearbox’s condition.

Monitoring the actual loads that occur in wind turbine gearboxes allows ZF and Schaeffler to create a basis on which new data-based models can be developed. In the first stage, the rating life and the static load safety factors of the gearbox bearings are assessed based on the available input variables using the virtual twin. In the subsequent stage, both fatigue and further damage mechanisms can be taken into consideration.

This is necessary because the operating life of rolling bearings in wind power applications is generally not limited by conventional material fatigue but by surface-initiated damage (e.g. due to excessive wear or lubricant contamination).

The load data-based assessment of the calculation resulting from the virtual twin will in future make it possible for harmful operating conditions to be detected early, allowing the lead time for initiating maintenance measures to be significantly shortened compared to existing condition monitoring systems.

It will additionally be possible to initiate suitable countermeasures to prevent critical operating conditions and thus extend the bearings’ operating life. Wind farm operators will thus have a tool at their disposal that significantly extends their planning horizons.

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