A Wasserstein generative digital twin model in health monitoring of rotating machines

Artificial intelligence-based rotating machine health monitoring and diagnosis methods often encounter prob-lems, such as a lack of faulty samples. Although the simulation-based digital twin model may potentially alleviate these problems with sufficient prior knowledge and a large amount of time, the more demanding requirements of adaptivity, autonomy, and context-awareness may not be satisfied. This study attempted to address these problems by proposing a novel digital twin model referred to as the Wasserstein generative digital twin model (WGDT). The model employs a Wasserstein generative adversarial network (WGAN) as its core to model virtual samples with high fidelity to the healthy physical samples obtained from different industrial assets, thereby meeting the adaptivity requirement. Further, through a designed consistency test criterion mechanism, samples with high fidelity were generated by checking the similarity of distributions between generated samples and healthy physical samples to ensure that training process in conducted in a timely manner and manual involvement is avoided, thereby catering to the need for autonomy. This mechanism is based on the synchronous evolution of the generator and critic during training. Furthermore, the structure of the critic network can be customized according to the service-end tasks and testing conditions, thereby fulfilling the context awareness requirement. Subsequently, the critic network in the Wasserstein generative adversarial network (WGAN) can be used to perform different service-end tasks. The performance of the digital twin model was evaluated using two experimental cases and the results indicated that the WGDT model can efficiently and stably perform service-end tasks such as health monitoring, early fault detection, and degradation tracking without the requirement of prior knowledge, historical test samples, and faulty samples regarding the asset.