Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Abstract

Abstract As a transmission component, the gear has been obtained widespread attention. The remaining useful life (RUL) prediction of gear is critical to the prognostics health management (PHM) of gear transmission systems. The digital twin (DT) provides support for gear RUL prediction with the advantages of rich health information data and accurate health indicators (HI). This paper reviews digital twin-driven RUL prediction methods for gear performance degradation, from the view of digital twin-driven physical model-based and virtual model-based prediction method. From the view of the physical model-based one, it includes a prediction model based on gear crack, gear fatigue, gear surface scratch, gear tooth breakage, and gear permanent deformation. From the view of the digital twin-driven virtual model-based one, it includes non-deep learning methods and deep learning methods. Non-deep learning methods include the wiener process, gamma process, hidden Markov model (HMM), regression-based model, and proportional hazard model. Deep learning methods include deep neural networks (DNN), deep belief networks (DBN), convolutional neural networks (CNN), and recurrent neural networks (RNN). It mainly summarizes the performance degradation and life test of various models in gear and evaluates the advantages and disadvantages of various methods. In addition, it encourages future works.