# Summarization of Remaining Life Prediction Methods for Special Power Plants
## 2 Research Status at Home and Abroad
### State of Research on Dynamic Data-Driven RUL Prediction Methods
The existing studies focus on the effect of network training rather than the time-varying stochastic and dynamic characteristics inherent in equipment performance degradation. Transfer learning is the application of knowledge or patterns learned from the same type of equipment to new equipment or new problems, focusing on how to map the data in the source domain and the target domain from the original feature space to the target feature space and better utilize the existing labeled data samples in the source domain for RUL prediction [88,89]. RUL prediction research based on transfer learning has the following characteristics [90,91]:  
1. Transfer learning can ignore the requirements regarding the same feature distribution of the model data and application data in traditional degradation models and data-driven models. The full-life performance degradation data of similar equipment types can be utilized, and their features can be transferred to the historical data of the target equipment through learning to realize the RUL prediction of the target equipment. 2. In this stage, transfer learning mainly focuses on research based on the deep transfer model and feature transfer model. The former reduces the complexity of the deep network to a certain extent, but the transfer effect mostly depends on the quality of the trained network. The latter requires prior knowledge of the characteristic differences in data distribution between the domains and an exploration of suitable mapping models. There are fewer reasonable explanations for transfer components and transfer tasks, and the theoretical research base is weak. 3. The effect of transfer learning is related to factors such as the volume of data, the quality of data, and the selection of transfer source domains. The applicability of the model under different conditions varies to some extent, and an adaptive adjustment of the transfer parameters is mostly used at present, which leads to a lack of theoretical explanations for the parameters of the model. 4. If there are large, dynamic changes in the learning source domain and target prediction domain of transfer learning, the iterative updating performance of transfer learning will become weak, resulting in a low prediction performance. The addition of an intermediate domain can reduce the impacts of dynamic changes on the transfer learning model, but the intermediate domain increases the complexity of the model; thus, the balancing of these two factors is a research focus at present. In summary, the existing literature shows that one can obtain more accurate prediction results through data feature extraction, health indicator construction, and RUL prediction modeling, but the hyperparameters and network design are difficult, and the generalization of the model is poor. Additionally, the prediction results ignore the internal degradation mechanism of mechanical equipment and lack a strict dynamic theoretical basis and interpretability. Furthermore, the method requires a large amount data in order to train the network, which limits its use in mechanical equipment for which one cannot obtain a large amount of monitoring data or the expected prediction results. Therefore, the fusion of dynamic models and data and the study of data generation methods have become new trends in research on the RUL prediction of mechanical equipment [92,93].