# Summarization of Remaining Life Prediction Methods for Special Power Plants
## 3 Future Research Directions
### RUL Prediction Method Driven by Data-Model Fusion  
Focusing on the problems of the interpretability and generalization of RUL prediction, typical health indicators were used to characterize the health status of special power plants, and the construction method for health indicators under multi-factor coupling excitation was studied to explore the quantitative expression method for the health status of special power plants. Based on this aim, to study the RUL prediction method for special power plants, a parametric degradation process model was constructed with dynamic data-model fusion, and the model uncertainty and data uncertainty were quantitatively analyzed. The influences of the uncertainty factors on the prediction accuracy were explored. Research was conducted on the construction method of the data-model fusion RUL prediction model based on complex task profiles, the real-time correction mechanism of dynamic data for the prediction models was analyzed, and a real-time prediction method for the RUL of special power plants was explored. The key research contents can be summarized according to the following three aspects:  
1. Research on the construction method of health indicators under multi-factor coupling excitation;
2. Research on the parameterized RUL prediction model based on health indicators;
3. Research on the real-time prediction method of RUL based on complex task profiles. The detailed technical route is shown in Figure 13.  
(1) Study of health indicators under multi-factor coupling incentives  
The operation data of the special power plant, obtained using the soft sensor method of dynamic parameters with follow-up, are periodic time series of vibration, impact, and pressure with noise interference, which are filtered and de-noised according to the operating frequency to form the vibration data set, impact data set, and pressure data set. The health indicator (HI) construction procedure of this institution is as follows:  
1. The dynamic excitation transfer matrix constructed based on the research contents described in Section 3.2 is used to infer the actual dynamic data of the device. 2. Considering the degradation process of the dynamic performance of the device as a random process, the parameter variables of the stochastic processes are taken as the health indicators of institutions. Vibration HI, impact HI, pressure HI, etc., are extracted based on the vibration data set, impact data set, and pressure data set, respectively. These HIs have practical physical significance, and the change process is highly interpretable. 3. The parameter matrix is constructed using each HI constructed based on the random process parameter (b), and the eigenspace is constructed by solving the eigenvalues of the parameter matrix. Figure 13: Technical route of the RUL prediction method driven by data–model fusion. 4. After optimizing the feature space with the parameters, HI can be fused with multiple parameters. This parameter contains the feature information on dynamic performance degradation, and the degree of degradation is positively related to its value. 2. Research on the RUL parametric prediction model based on health indicators  
Based on the multi-parameter fusion HI obtained through the above process, the parametric prediction model of data-model fusion can be obtained by substituting it into the multi-parameter fusion degradation model that is constructed. In order to preserve generality, the individual differences incorporated into the degradation model are reflected in the uncertainty of stochastic degradation processes based on the dynamic model. At the same time, the multi-parameter fusion HI, based on dynamic data, also contains measurement errors, which manifest as data uncertainty. These two kinds of uncertainty can be treated according to a mutually independent normal distribution, that is, the model uncertainty f(x) \(\sim\) N (\(\mu_{\text{m}}\), \(\sigma_{\text{m}}\)) and the data uncertainty g(x) \(\sim\) N (\(\mu_{\text{d}}\), \(\sigma_{\text{d}}\)).