RUL Estimation for Rolling Bearings Using Augmented Quaternion-Based Least Mean P-Power With Correntropy Induced Metric Under Framework of Sparsity

Accurate remaining useful life (RUL) estimation of rolling bearings can significantly improve the safety, availability, reliability and productivity of a rotating machinery system. In this article, a new approach of RUL estimation for rolling bearings based upon the fractional-order spatiotemporal sparse low-rank matrix (FST-SLRM) and augmented quaternion least mean p-power with correntropy induced metric (AQLMP-CIM) is originally proposed. Specifically, the proposed approach includes three modules: the construction of the fused health indicator, which fuses the classical features and spectral kurtosis derived indices; the decomposition of the static and dynamic components, the fused health indicator time series (FHITS) is decomposed by the FST-SLRM algorithm, thus low- and high- frequency trend components (i.e., LFCs, HFCs) are obtained, respectively; and the prognostics of degradation trajectory and RUL estimation, the degradation trajectories are obtained by the AQLMP-CIM algorithm via integrating the predicted LFC and HFC, and the RUL is eventually acquired. Two experimental cases are conducted on run-to-failure fatigue test of rolling bearings, the results demonstrate that the proposed approach is able to achieve better performance compared with state-of-the-art benchmarks.