Bearing performance degradation prediction based on Teager energy operator and multi-stage degradation model

Abstract

This paper constructs a degradation state judgment function and accurately characterizes the degradation trajectories under different failure modes based on the multi-stage degradation model, so as to achieve accurate prediction of the degradation state of the product at each moment. Firstly, the Fourier series expansion is used to extract the intrinsic band function of the signal, the total energy of the signal is calculated based on the Teager energy operator in different frequency bands, and the recognition function of the degradation state is constructed based on the first-order and second-order derivatives of the energy operator. Secondly, the judgment function is used to determine the moment of sudden change of the degradation state, and the multi-stage degradation model is constructed based on the nonlinear Wiener process, which is used to map the characteristics of the multi-stage degradation of the bearings. Finally, the degradation state judgment function is verified to be able to accurately identify the moment of degradation state mutation according to the real degradation data of the bearings, and it also proves that the multi-stage degradation model can more accurately characterize and predict the degradation state of the bearings in various moments.