Coupling Interface Load Identification of Sliding Bearing in Wind Turbine Gearbox Based on Polynomial Structure Selection Technique

Sliding bearings are widely used in wind turbine gearboxes, and the accurate identification of coupling interface loads is critical for ensuring the reliability and performance of these systems. However, the space–time coupling nature of these loads makes them difficult to calculate and measure directly. An improved method utilizing the POD decomposition algorithm and polynomial selection technology is proposed in this paper to identify the sliding bearing coupling interface loads. By using the POD decomposition algorithm, the sliding bearing coupling interface loads can be decomposed into the form of a series of independent oil film time history and spatial distribution functions. Then, it can be converted into space–time independent sub-coupled interface load identification in which oil film time history can be transformed into the recognition of a certain order modal load and the corresponding oil film spatial distribution function can be fitted with a set of Chebyshev orthogonal polynomial. To address the ill-posedness caused by the weak correlation between the modal matrix and polynomial options during the identification process, this paper introduces polynomial structure selection technology. Firstly, displacement responses are collected, and a series of modal loads are identified using conventional concentrated load identification methods. Then, the polynomial structure selection technology is applied to select the effective modal shape matrix, using a specific mode load as the oil film time history function. The load ratios of other mode loads to this reference mode load are compared, and the effective Chebyshev orthogonal polynomials are selected based on the error reduction ratio. Finally, multiplying the identified oil film time histories by the corresponding oil film spatial distribution functions yields the coupling interface load. The results of the numerical examples verify the improved method’s rationality and effectiveness.