A novel based-performance degradation indicator RUL prediction model and its application in rolling bearing

Aiming at the problem of poor prediction performance of rolling bearing remaining useful life (RUL) with single performance degradation indicator, a novel based-performance degradation indicator RUL prediction model is established. Firstly, the vibration signal of rolling bearing is decomposed into some intrinsic scale components (ISCs) by piecewise cubic Hermite interpolating polynomial-local characteristic-scale decomposition (PCHIP-LCD), and the effective ISCs are selected to reconstruct signals based on kurtosis-correlation coefficient (K-C) criteria. Secondly, the multi-dimensional degradation feature set of reconstructed signals is extracted, and then the sensitive degradation indicator IICAMD is calculated by fusing the improved independent component analysis (IICA) and Mahalanobis Distance (MD). Thirdly, the false fluctuation of the IICAMD is repaired by using the gray regression model (GM) to obtain the health indicator (HI) of the rolling bearing, and the start prediction time (SPT) of the rolling bearing is determined according to the time mutation point of HI. Finally, generalized regression neural network (GRNN) model based on HI is constructed to predict the RUL of rolling bearing. The experimental results of two groups of different rolling bearing data-sets show that the proposed method achieves better performance in prediction accuracy and reliability.