Health Prediction of Shearers Driven by Digital Twin and Deep Learning

In view of the difficult problems of condition monitoring and maintenance of shearers in poor working environment, combined with the high-fidelity behavior simulation characteristics of digital twin and the powerful data mining ability of deep learning, a prediction method of shearer health status driven by the fusion of digital twin and deep learning was proposed. The digital twin of shearers was constructed based on multi-physical parameters of physical space, and the health state early prognosis of shearers was realized by visual display and analysis in virtual space. A prediction model of RUL of shearer key parts was established based on deep learning, and the online RUL prediction of parts driven by real-time monitoring data was realized. The prediction of shearer health was obtained based on the status of digital twin and the value of RUL. Finally, the effectiveness of the method was verified by experiments, which provides a new idea for the monitoring and management of the health status of the shearers. © 2020, China Mechanical Engineering Magazine Office. All right reserved.