Digital twin aided adversarial transfer learning method for domain adaptation fault diagnosis

Machine health management has become the focus of equipment monitoring upgrading with the advance of digital twin (DT). The DT model is able to generate system performance data that is close to reality, which opens a new way for the cyber-physical integration of equipment monitoring. Furthermore, it also provides a significant opportunity for mechanical fault diagnosis when the collected fault signals are insufficient. In this paper, a DT aided intelligent fault diagnosis model is proposed for triplex pump. Specifically, the simulation model of the triplex pump is built by Simscape in MATLAB, and the measured simulation data is continuously updated to construct the DT model. Then a novel transfer learning model based on domain-adversarial strategy and Wasserstein distance is present and trained by the source domain data which generated from the DT model. Next, the opening pressure of the triplex pump is controlled to simulate different working conditions, so as to achieve feature transfer and fault diagnosis for the DT model. The experimental results show that the proposed method is effective and superior to other advanced transfer learning methods.