# Summarization of Remaining Life Prediction Methods for Special Power Plants
## 2 Research Status at Home and Abroad
### State of Research on Dynamic Model-Driven RUL Prediction Methods
At present, using the ablation degradation model, the friction degradation model can be used to quantitatively or, in part, qualitatively analyze the impacts of different matrix materials, coatings, surface structures, and physical strengthening methods on the degradation process of structural performance [41]. The intuitive manifestations of this structural performance degradation are the change in the contact crash feature results and the mapping relationship with contact crash friction wear [42], which are also key research directions in this stage. Research on the optimization characteristics of feature structures is based on suggestions regarding the above three aspects, aiming to change the structural morphology and strengthen the material properties of structures through physical-chemical means, from both macro- and micro-perspectives, in order to achieve the goal of slowing down the degradation process of special power plants [43; 44]. The analysis of the vibration characteristics of special beams focuses on the following aspects: the correction of the dynamic differential solution method, extended based on the variational principle [45; 46]; sensitivity analysis under coupling constraint and time-varying geometric characteristics [47]; the beam characteristic analysis of feature abstraction [48; 49; 50]; and coupling analysis based on structural correlation characteristics [51; 52; 53]. With further PHM research on mechanical equipment, basic theories such as fracture mechanics, fatigue damage, corrosion, and wear are constantly being enriched [13; 14; 54], and the classical Paris model [55] and Forman model [56] have been improved. New technologies and methods have been developed for material and strength testing, safety monitoring and control, and reliable operation and maintenance [1; 2]. A series of standards, such as the environmental test method (MIL-STD-810G), dynamic environmental criteria (NASA-HDBK-7005), condition monitoring and diagnosis (GB/T 23713.1-2009), and RUL assessment (GB/T 34631-2017), have been proposed in the field of engineering applications [57; 58; 59; 60]. When mechanical equipment has a simple composition and a single degradation factor, the prediction accuracy of the model-driven RUL is high [55]. However, with an enhancement of the structural composition and the complexity of the task profile, the time-varying nature and uncertainty of the degradation process increase, making it difficult to obtain an accurate model and achieve high-precision model-driven prediction results. To describe the time-varying and uncertainty qualities of the mechanical equipment degradation process, stochastic degradation models based on the framework of probability theory are introduced into RUL prediction, with the main methods including the Wiener process [61; 62], gamma process [63; 64], etc. The main idea of such prediction methods is to define RUL as the first arrival time at which the stochastic process reaches the failure threshold and to achieve RUL prediction by solving the probability distribution of the first arrival time. The Wiener process is suitable for describing the non-monotonic degradation process of mechanical equipment caused by a large number of small losses. In order to effectively reflect the non-Markov and multimodal properties of the degradation process, the degradation model based on fractional Brownian motion has become a focal point of research on the current Wiener process lifetime prediction theory [65; 66]. The gamma process is used to describe mechanical equipment's strictly monotonous degradation process and can better depict the slow degradation caused by continuous small impacts and the damage caused by a large impact, but the increments in the gamma process are related to a gamma distribution, and the mathematical form is complex, making real-time parameter estimation difficult; thus, it is mainly used to analyze the common lifetime characteristics of similar equipment.