Virtual Data Generation for Marine Turbochargers Using GANs

Abstract

A large amount and diversity of data is required for training an effective and robust deep learning or machine learning model. However, obtaining sufficient and various real-time data is difficult for bulky equipment such as marine turbochargers in practical working conditions, since exhaustive acquisition and annotation is hard and time-consuming. In this paper, the proposed methods use Generative Adversarial Networks (GANs) to generate virtual numerical data from limited number of real data. Two GAN architectures, which are basic GAN and conditional GAN, are utilized to develop the methods. The resultant virtual data can be either unlabelled or labelled. It shows that GANs can generate numerical data which is similar enough and not significantly different from the real data. Furthermore, GAN-based domain adaptation approach will be developed to generate synthetic data for predictive maintenance of a turbocharger, given by some healthy data of itself and laboratory testing data of other turbochargers.