Remaining useful life prediction of rolling bearings based on performance evaluation and multifractional generalized Cauchy model with adaptive drift

The proposed Remaining Useful Life (RUL) prediction framework utilizes several advanced techniques to accurately estimate the remaining service life of rolling bearings. The framework includes early failure assessment, adaptive failure threshold (FT) determination, and a multifractional generalized Cauchy model (MfGC). The early failure assessment is enabled by establishing early FTs and health indicator (HI) curves generated by the Mahalanobis distance cumulative sum (MD-CUSUM) technique. The proposed dynamic fault threshold update method uses the BOX-COX transformation and Chebyshev inequality to determine confidence intervals for evaluating the fault threshold time. The multifractional nature of the MfGC process is characterized by independent, time-varying Hurst indices and fractional dimensions, and the long-range dependence (LRD) characteristics and stochasticity of the process are explained by the diffusion terms generated from the MfGC differential time series. The MfGC model with adaptive drift is constructed for various degenerate trajectories, and a method for estimating the model's parameters is proposed. The effectiveness of the proposed RUL prediction method is demonstrated using the XJTU-SY bearing dataset.