Data-driven wear monitoring for sliding bearings using acoustic emission signals and long short-term memory neural networks

Driven by the potential applications of sliding bearings in planetary gearboxes for wind turbines, the wear prognosis of heavy loaded sliding bearings under low rotational speeds is an important aspect. The aims of this study are to identify an adequate condition monitoring technique and demonstrate the potential of data-driven wear monitoring for scenarios, where transient wear data for data-driven monitoring is not available. In a first step, Acoustic Emission (AE) technique has been applied to a special test rig for planetary gearbox sliding bearings. It was demonstrated that AE can be used to distinguish between wear-critical mixed friction and hydrodynamic regime. In a second step, a data-driven method for wear monitoring was developed and applied to a component test rig for sliding bearings. For validation and generation of condition monitoring data, sliding bearings from the same bronze material were subjected to steady speed conditions in mixed lubrication regime with different loads as well as runtime. The results from these experiments and results from validated physical wear simulations are the input parameters for the proposed data-driven approach. The wear prognosis is performed with recurrent neural networks, which can predict the transient degradation. With the developed data-driven approach to wear monitoring, a good accuracy can be achieved that is capable of real-time wear monitoring. •Suitability of AE to efficiently detect mixed friction conditions in gearbox sliding bearings.•Data-driven wear monitoring approach for the reliability assessment of sliding bearings.•Use of validated physical wear simulations for the training of a machine learning model.