Utilizing VQ-VAE for End-to-End Health Indicator Generation in Predicting Rolling Bearing RUL
This addresses the need for better health indicators in industrial maintenance, but it is incremental as it builds on existing VQ-VAE and prediction models.
The paper tackled the problem of predicting the remaining useful life (RUL) of rolling bearings by introducing an end-to-end health indicator generation method using VQ-VAE, which achieved lower values for novel metrics like mean absolute distance (MAD) and mean variance (MV) on the PMH2012 dataset.
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.