The prediction of the remaining useful life (RUL) of rolling bearings is a pivotal issue in industrial production. A crucial approach to tackling this issue involves transforming vibration signals into health indicators (HI) to aid model training. This paper presents an end-to-end HI construction method, vector quantised variational autoencoder (VQ-VAE), which addresses the need for dimensionality reduction of latent variables in traditional unsupervised learning methods such as autoencoder. Moreover, concerning the inadequacy of traditional statistical metrics in reflecting curve fluctuations accurately, two novel statistical metrics, mean absolute distance (MAD) and mean variance (MV), are introduced. These metrics accurately depict the fluctuation patterns in the curves, thereby indicating the model's accuracy in discerning similar features. On the PMH2012 dataset, methods employing VQ-VAE for label construction achieved lower values for MAD and MV. Furthermore, the ASTCN prediction model trained with VQ-VAE labels demonstrated commendable performance, attaining the lowest values for MAD and MV.

以振动信号为基础，构建了一种使用矢量量化变分自动编码器(VQ-VAE)的健康指标(HI)构建方法，引入了均值绝对距离(MAD)和均值方差(MV)两个新的统计度量来准确描述振动曲线波动的模式，通过在PMH2012数据集上的实验得出，使用VQ-VAE标签进行训练的ASTCN预测模型表现出了卓越的性能。

利用VQ-VAE进行端到端的健康指标生成用于滚动轴承剩余寿命预测