Multivariate time series generation based on dual-channel Transformer conditional GAN for industrial remaining useful life prediction

Abstract

Remaining useful life (RUL) prediction is a key enabler of predictive maintenance. While deep learning based prediction methods have made great progress, the data imbalance issue caused by limited run-to-failure data severely undermines their performance. Some recent works employ generative adversarial network (GAN) to tackle this issue. However, most GAN-based generative methods have difficulties in simultaneously extracting correlations of different time steps and sensors. In this paper, we propose dual-channel Transformer conditional GAN (DCTC-GAN), a novel multivariate time series (MTS) generation framework, to generate high-quality MTS to enhance deep learning based RUL prediction models. We design a novel dual-channel Transformer architecture to construct the generator and discriminator, which consists of a temporal encoder and a spatial encoder that work in parallel to automatically pay different attention to different time steps and sensors. Based on this, DCTC-GAN can directly extract the long-distance temporal relations of different time steps while capturing the spatial correlations of different sensors to synthesize high-quality MTS data. Experimental analysis on widely used turbofan engine dataset and FEMTO bearing dataset demonstrates that our DCTC-GAN significantly enhances the performance of existing deep learning models for RUL prediction, without changing its structure, and exceeds the capabilities of current representative generative methods.