HYGP-MSAM based model for slewing bearing residual useful life prediction

Abstract

Slewing bearings are critical functional components of large machinery and their residual useful life (RUL) prediction can avoid downtime and reduce accidents and casualties. In the field of their condition monitoring and life prediction, multi-signal and multi-feature fusion (MSMFF) is a trend for over the current literatures. However, most of the existing researches only consider the independent effect of degradation indicators, thereby ignoring the coupling effect between different signals. To overcome this gap and further compensate for the lacks of transparency and practical meaning in data-driven approaches, especially for artificial intelligence ones, this paper proposes an adaptive symbolic regression based modeling strategy: hybrid genetic programming-model structure adaptive method (HYGP-MSAM), integrating the strengths of HYGP algorithm which is a realization based on symbolic regression directly obtaining explicit analytical expressions for the life model compared with “black box” modeling methods and MSAM aiming for reconstructing the initial models with coupling terms. To get better description of degradation trend, ensemble empirical mode decomposition combined with singular value decomposition (EEMD-SVD) denoising method is employed for raw signals and degradation indicators are obtained through a manifold learning based fusion algorithm. The proposed HYGP-MSAM modeling strategy is utilized to establish life model expressions afterwards. Finally, life models in the form of function expressions are derived and an accelerated run-to-failed experiment is carried out to test this strategy. It is shown that adaptive coupling reconstruction strategy for upgrading the symbolic regression based modeling methods can greatly improve the fault tolerance of algorithms under parametric error and effectively improve the prediction accuracy.