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A novel based-performance degradation indicator RUL prediction model and its application in rolling bearing
ISA Transactions ( IF 7.3 ) Pub Date : 2021-03-31 , DOI: 10.1016/j.isatra.2021.03.045
Chuangyan Yang 1 , Jun Ma 1 , Xiaodong Wang 1 , Xiang Li 1 , Zhuorui Li 1 , Ting Luo 1
Affiliation  

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.



中文翻译:

一种新的基于性能退化指标RUL预测模型及其在滚动轴承中的应用

针对性能退化指标单一的滚动轴承剩余使用寿命(RUL)预测性能较差的问题,建立了一种新的基于性能退化指标的RUL预测模型。首先,通过分段三次厄米插值多项式-局部特征-尺度分解(PCHIP-LCD)将滚动轴承的振动信号分解为一些固有尺度分量(ISC),并根据峰度相关性选择有效的ISC来重构信号。系数 ( K - C ) 标准。其次,提取重构信号的多维退化特征集,进而得到敏感退化指标IICAMD通过融合改进的独立分量分析 (IICA) 和马氏距离 (MD) 来计算。第三,利用灰色回归模型(GM)修复IICAMD的虚假波动,得到滚动轴承的健康指标( HI),并根据时间确定滚动轴承的启动预测时间(SPT)。HI的突变点。最后,构建基于HI的广义回归神经网络(GRNN)模型来预测滚动轴承的RUL。两组不同滚动轴承数据集的实验结果表明,所提方法在预测精度和可靠性方面取得了较好的表现。

更新日期:2021-03-31
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