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.

翻译：滚动轴承剩余使用寿命（RUL）预测是工业生产中的关键问题。解决该问题的重要方法之一是将振动信号转化为健康指标（HI）以辅助模型训练。本文提出了一种端到端的健康指标构建方法——向量量化变分自编码器（VQ-VAE），该方法解决了传统无监督学习方法（如自编码器）中潜变量降维的需求。此外，针对传统统计指标在精确反映曲线波动方面的不足，引入了两种新型统计指标——平均绝对距离（MAD）和平均方差（MV）。这些指标能够精确描述曲线的波动模式，从而表征模型在区分相似特征方面的准确性。在PMH2012数据集上，采用VQ-VAE进行标签构建的方法取得了更低的MAD和MV值。此外，基于VQ-VAE标签训练的ASTCN预测模型展现出良好性能，获得了最低的MAD和MV值。