Synthetic Data Generation Using GAN for RUL Prediction of Supercapacitors

Abstract

The remaining useful life (RUL) prediction of supercapacitors is an important part of supercapacitors management system. To accurately predict the RUL of supercapacitor, a large amount of capacity data is required which can be difficult to acquire due to privacy restrictions and limited access. Previous works have employed the use of deep learning models to synthetically generate data. However, a prerequisite ensuring the success of these models depends on their ability to preserve the temporal dynamics of the data. This paper presents a generative adversarial network (GAN) for synthetic data generation and a long short-term memory (LSTM) network for accurate RUL prediction. Firstly, the GAN model is employed for synthetic data generation and LSTM for RUL prediction. We show that the GAN model is capable of preserving the temporal dynamics of the original data and also prove that the generated data can be used to accurately carry out RUL prediction. Our proposed GAN model was able to achieve an accuracy of 85% after 500 epochs. The performance of the generated data set with the LSTM model achieved an RMSE of 0.29. The overall results show that synthetic data can be used to achieve excellent performance for RUL prediction.