Joint training of a predictor network and a generative adversarial network for time series forecasting: A case study of bearing prognostics

Abstract

The lack of bearing run-to-failure data has been one of the challenges in developing and practically implementing robust bearing prognostics models. This paper proposes a new Generative Adversarial Network (GAN) based prognostics method for RUL prediction. We propose a novel joint training strategy to integrate the training process of a bearing health predictor within the GAN architecture. GAN uses available time series degradation data to generate synthetic degradation data that enhances the predictor’s learning and forecast performance, thus improving the RUL prediction accuracy. We demonstrate the utility and performance of the proposed method through two examples. The first numerical toy case study of forecasting polynomial-like time series shows that the proposed Jointly Trained Health Predictor (HP-JT) method produces smaller one- and multi-step-ahead prediction errors than a traditional health predictor (HP). In the second case study, we design a cross-validation study utilizing an open-source bearing dataset to evaluate the model’s performance in RUL prediction. Compared to HP, the proposed method decreases the bearing RUL prediction average error by 29.4% in a five-fold cross-validation study. We further compare the model with standard data augmentation techniques such as adding noise and using a variational autoencoder (VAE). The results from the case studies show that the proposed method can generate time series representing the real-data distribution.