Digital Twin-Enabled Health Prognostics for Smart Manufacturing Systems Under Uncertain Operating Conditions

Health prognostics for the machinery is a key objective of condition-based maintenance, with its primary goal being the remaining useful life (RUL) prediction. To efficiently structure prognostic data and address the challenges associated with uncertain operating conditions (OCs), this article introduces the digital twin (DT)-enabled RUL prediction system in smart manufacturing. The system mainly includes three layers, i.e., physical infrastructure layer (PIL), information interaction layer (IIL), and DT service layer (DT-SL). In the PIL, the multisource data are generated from different equipment and then transmitted to the IIL. In the IIL, the DT health prognostics information model is designed to organize the prognostic data in a structured manner. In the DT-SL, the virtual model, prognostic model, and sample generation model are constructed for the visualization of prognostic data, RUL prediction under uncertain OCs, and model validation. Finally, the effectiveness of the proposed system is experimentally demonstrated through two industrial cases, highlighting efficient prognostic data organization and accurate degradation tracking under uncertain OC scenarios.