# Summarization of Remaining Life Prediction Methods for Special Power Plants
## 2 Research Status at Home and Abroad
### State of Research on Data-Model-Fusion-Driven RUL Prediction Methods  
With the continuous development of computer science, new application scenarios for integrating computational mechanic models and data science are constantly emerging. There are three main means of fusion [94]: The first method directly applies intelligent algorithms to solve linear dynamic models but still has a certain improvement interval in its ability to determine efficiency and limitations [95]. The second type produces a wide range of sample data through a dynamic model and uses an intelligent algorithm to extract the abstract mapping relations of the data combined with multi-source data [96, 97, 98]. The third is based on a \"data driven\" framework of computational mechanics, using random filtering (KF/EKF/UKF/PF) as a bridge and health indicators based on the characteristics of the project to select a suitable degradation model. The fusion filtering method is a random or parameter identification method used to determine the model's parameters. Through the model's extrapolation prediction results and the complementary advantages of implementing data mode methods, the prediction accuracy can be improved [99]. Kalman filtering (KF) addresses the optimal estimation problem of the linear system state with Gaussian noise for input and output data. This optimal estimation can be regarded as a filtering process because system noise and interference are included in the observed data. Both extended Kalman filtering (EKF) and unscented Kalman filtering (UKF) can handle nonlinear degradation processes with Gaussian noise. Of these methods, EKF establishes the Jacobi determinant based on the partial derivative of the nonlinear degradation process and then realizes linearization processing. UKF realizes an approximate calculation of the nonlinear degradation process via untrace transformation. The basic idea of this kind of method is to assume that the mechanical equipment degradation conforms to a certain degradation mechanism model, and the mathematical model and key parameters are fused into a discrete-time-state space model by establishing an extended vector [100]. Then, KF/EKF/UKF is used to realize the state updates and prediction. When studying RUL predictions of the rotation mechanism, the exponential function and its extension model [101] and the fusion curve model [102] are often selected as degradation mechanism models. Particle filter (PF) is a recursive Bayesian algorithm based on the Monte Carlo method. It uses sequential importance sampling to provide an approximate solution to the Bayesian optimal solution and is suitable for describing a nonlinear degradation process with non-Gaussian random noise. There are three main ideas aiming to improve the life prediction accuracy of the PF method: the selection of an appropriate degradation model [101], the improvement of PF [103], and integration with other methods [104]. With the development of machine learning, fusion research directly using machine learning and degradation models is also an important direction for data-model fusion. Its basic idea is based on multi-source sensor monitoring data, the construction of a virtual health indicator via weighted fusion, the use of stochastic process matching with a time-varying evolution trend, the optimization and adjustment of the fusion model and parameters using life prediction deviation to achieve an accurate prediction, and the quantitative analysis of forecast uncertainty [25; 105; 106; 107]. However, the weighted fusion health indicators rely too much on subjective experience and lack objectivity in weight determination, which makes them unsuitable for the real-time prediction of mechanical equipment RUL. According to the existing literature, the prediction accuracy of RULs driven by data-model fusion depends on three aspects: the selection of the stochastic degradation model, the determination of the fusion method, and the size of the data.