A novel based-performance degradation indicator RUL prediction model and its application in rolling bearing. (February 2022)

• Record Type: Journal Article
• Authors: Yang, Chuangyan; Ma, Jun; Wang, Xiaodong; Li, Xiang; Li, Zhuorui; Luo, Ting
• Is Part Of: ISA transactions. Volume 121(2022)
• Page Start: 349
• Page End: 364
• Publication Date: 2022-02
• Subjects: Rolling bearing -- Feature fusion -- Health indicators -- GRNN -- RUL prediction; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2021.03.045
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: 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. Highlights: A sensitivity degradation indicator IICAMD is calculated by improved ICA and MD. GM method is used to repair the false fluctuation of IICAMD, and the degradation indicator HI is obtained. A novel prediction model based on HI-GRNN is proposed/constructed to predict RUL. The reliability of the presented method is confirmed through comparison experiment.