The performance degradation assessment (PDA) of rolling element bearings is a necessary link to ensure the reliability of high-end equipment. However, traditional health indicators (HIs) are not sensitive to early defects, and there are often large local fluctuations in the later stage of degradation. Hence, this paper propose a novel PDA method to obtain HIs with early warning capability and monotone trend. Firstly, an improved graph spectrum reconstruction method is proposed to enhance the characteristics of signals. The random phase space reconstruction strategy is introduced to solve the problem of large-scale graph Laplacian matrix decomposition. Then, the spectrums of the enhanced signals are characterized, namely the high-dimensional degradation features in frequency domain are extracted and smoothed by Kalman filter. Finally, the Laplacian Eigenmaps is used to extract the intrinsic degeneration manifolds from the high-dimensional degradation features as the established HIs. Life cycle degradation data and quantitative failure data are analyzed to verify the effectiveness of the proposed method. Compared with other state-of-art methods, the results show that the HIs established by the proposed method reflect the degradation earlier and have obvious degradation trend. It effectively realizes the mapping between degradation and HI.

滚动轴承的性能退化评估（PDA）是确保高端设备可靠性的必要环节。但是，传统的健康指标（HIs）对早期缺陷不敏感，并且在退化的后期通常会出现较大的局部波动。因此，本文提出了一种新颖的PDA方法来获得具有预警能力和单调趋势的HI。首先，提出了一种改进的图谱重构方法，以增强信号的特性。为了解决大规模图拉普拉斯矩阵分解问题，引入了随机相空间重构策略。然后，表征增强信号的频谱，即通过卡尔曼滤波器提取并平滑频域中的高维劣化特征。最后，拉普拉斯特征图用于从高维退化特征中提取固有的退化流形，作为已建立的HI。分析了生命周期退化数据和定量失效数据，以验证该方法的有效性。与其他现有方法相比，结果表明，该方法建立的HI较早地反映了降解，并具有明显的降解趋势。它有效地实现了退化与HI之间的映射。结果表明，该方法建立的HIs较早地反映了降解，并具有明显的降解趋势。它有效地实现了退化与HI之间的映射。结果表明，该方法建立的HIs较早地反映了降解，并具有明显的降解趋势。它有效地实现了退化与HI之间的映射。