# Summarization of Remaining Life Prediction Methods for Special Power Plants
## 3 Future Research Directions
### Dynamic Model-Driven RUL Prediction Methods  
To construct a dynamic performance degradation model of multi-factor coupling excitation, multi-working conditions and a different combined structure are used. First, the degradation mechanism should be extracted as a reciprocating movement mechanism, combining the dynamic model with the stochastic degradation model, and one should study vibrations, shock, and stress as a single factor under the random vibration. Then, a dynamic stochastic degradation model is constructed under one-factor excitation. Next, the mechanism of multi-factor coupling is analyzed, and the stochastic degradation dynamics model under multi-factor coupling excitation is constructed. Through the extraction of multi-factor features, the dynamic profile and performance degradation law of the reciprocating motion mechanisms under multi-working conditions are analyzed, and the construction of a dynamic stochastic degradation model of series, parallel, and hybrid structures is explored to clarify the performance degradation mechanism. The key research contents can be summarized based on the following four aspects:  
1. A study was carried out on a stochastic degradation model of dynamics under single-factor stochastic excitation;
2. A study was conducted on a stochastic degradation model of dynamics under multi-factor coupling excitation;
3. A study was conducted on a random degradation model under multi-working conditions;
4. A study was conducted on a stochastic degradation model with a combinatorial structure. The implementation flowchart of RUL technology prediction using the model-driven dynamic method is shown in Figure 5.  
Figure 4: Overall technical route. 3.1.1. Dynamic Stochastic Degradation Process under Single-Factor Random Excitation  
By analyzing the actual operation process of a special power plant, it can be found that its internal rotating motion mechanism has a high level of reliability and is maintenance-free throughout the whole life-cycle. However, the internal reciprocating motion mechanism often undergoes fatigue damage and corrosion wear, which lead to the degradation of the dynamic performance. In extreme cases, the mechanism may be damaged. Through the analysis of the physical mechanism, the coupling effect of multiple dynamic excitation is induced (such as vibration, impact, pressure, etc. ); thus, the classical Paris model and Forman model cannot accurately describe the degradation process of the mechanism's dynamic performance. At the same time, the performance degradation of the reciprocating mechanism is caused by the comprehensive action of dynamic incentives, which should satisfy the infinite separability. However, the stochastic process models that mathematically satisfy the infinite separability are the Wiener process, gamma process, and inverse Gaussian process, and the degradation processes of different types of dynamic excitation are not the same. Therefore, the degradation model of a reciprocating motion mechanism using these three kinds of stochastic processes has strong explanatory power in terms of both statistics and dynamics. Starting from single-factor stochastic excitation, such as vibration, impact, and pressure, the stochastic degradation processes of different types of dynamic excitation are analyzed, and the performance degradation process of the reciprocating mechanism is studied in detail by modeling the stochastic degradation process caused by the single factor and identifying the model parameters. According to dynamic parameters such as vibration, impact, and pressure, the stochastic process model is constructed, being modeled through the expectation maximization (EM) algorithm so that the probability density distribution function and RUL distribution function of the single-factor stochastic degradation process can be obtained.