Domain adaptation digital twin for rolling element bearing prognostics

Abstract

Artificial Intelligence (AI) is escalating in data-driven condition monitoring research. Traditional expert knowledgebased Prognostics and Health Management (PHM) processes can be smartened up with the assistance of various AI techniques, such as deep learning models. On the other hand, current deep learning based prognostics suffers from the data deficit issue, especially considering the varying operating conditions and the degradation modes of the components in practical industrial applications. With the development of simulation techniques, physical-knowledge based digital twin models give engineers access to a large amount of simulation data at a lower cost. These simulation data contain the physical characteristics and the degradation information of the component. In order to accurately predict the Remaining Useful Life (RUL) during the degradation process, in this paper, a bearing digital twin model is constructed based on a phenomenological vibration model. A Domain Adversarial Neural Network (DANN) is used to achieve the domain adaptation target between the simulation and the real data. Regarding the simulation data as the source domain and the real data as the target domain, the DANN model is able to predict the RUL without any priori knowledge of the labelling information. Based on the validation results from real bearing run-to-failure experiments, the proposed method is able to get the minimum RUL prediction error compared against state-of-the-art methods.