Roller Bearing Fault Diagnosis Based on Partial Reconstruction Symplectic Geometry Mode Decomposition and LightGBM

Abstract

It is always a hot and challenging problem to extract the characteristic information of roller bearings from strong noise interference. Conventional Hilbert-Huang Transform (HHT), Local Mean Decomposition (LMD), Local Feature-Scale Decomposition (LCD), and so on have some issues like overenvelope, under-envelope, frequency-chaos, end-point effect, and so on. Symplectic Geometry Mode Decomposition (SGMD) is one of the most efficient approaches to reconstruct this model. But SGMD has a drawback that the computation efficiency is reduced quickly with an increase in the quantity of data, and the degradation precision is influenced by the non-valid Symplectic Geometric Component (SGC). On this basis, a Regularized Composite Multiscale Fuzzy Entropy (RCMFE) is proposed, which is used to estimate the complexity of the reconstructed original individual parts and restrict the minimum amount of remaining power. This paper presents a Partial Reconstruction Symplectic Geometry Mode Decomposition (PRSGMD) approach. The simulation results indicate that PRSGMD can not only enhance the precision of SGMD but also enhance its robustness and validity. Finally, a maximal distance evaluation technique (DET) is employed in combination with a more interpretable tree-based Light Gradient Boosting Machine (LightGBM) for the intelligence fault diagnosis for rolling bearings.