# Summarization of Remaining Life Prediction Methods for Special Power Plants
###### Abstract
actual dynamic parameters, such as the vibration, impact, pressure, etc., of the special power plants' internal mechanisms due to the complex transmission characteristics of the internal and external mechanisms. At this point, it is necessary to use soft-sensing technology to indirectly obtain dynamic data [15]. The so-called soft measurement method aims to obtain dynamic parameters that are difficult to measure for special power plants. Through the selection of auxiliary variables, data acquisition and processing, the construction of soft measurement models, and real-time correction, these parameters can be inferred or estimated [16]. Therefore, the question of how to soft-measure the dynamic parameters of special power plants and construct a mapping relationship between dynamic data and RUL is currently one of the key challenges in research [17]. 3. Compared with ordinary power plants, the operating conditions of special power plants have state characteristics such as time-varying and discontinuous status. Moreover, their dynamic parameter measurement data have the characteristics of being non-stationary, single-measurement data and imbalanced, multiple-measurement data [18]. These specific features result in the inability to accurately characterize the complexity and randomness of the degradation process of conventional power plants based on their mapping relationship with RUL. The mapping relationship between the dynamic data established based on conventional power plants and RUL lacks interpretability, generalization, etc. [19]. In view of this, by combining the dynamic data of special plants with their structural characteristics and integrating the dynamic model-driven RUL prediction method with the dynamic data-driven RUL prediction method, breakthroughs can be made in terms of the interpretability and generalization of the model, and the expected results can be obtained [20]. Therefore, the question of how to effectively integrate dynamic models and data in order to establish RUL prediction methods is currently one of the key challenges in research [21; 22]. In conclusion, the realization of RUL predictions for special power plants faces the following challenges: First, the question of how to construct a dynamic model-driven RUL prediction method to measure the effects of vibration, impact, and pressure on mechanism wear and damage. The second problem is that it is difficult to directly measure the internal dynamic parameters of the device. How can a soft sensor be used? How can a dynamic data-driven RUL prediction method be built based on soft sensor data? Third, how should dynamic models and data effectively be integrated to ensure the interpretability and generalization of the prediction model? In view of the above three challenges, national and international scholars have carried out research on the RUL prediction of rotary motion mechanisms based on degradation mechanisms [23], deep learning [6; 24], fusion methods [25], etc. However, they have encountered difficulties in exploring the RUL of reciprocating motion mechanisms. Based on \"internal damage\" phenomena such as wear and injury in the internal reciprocating motion mechanism of the special power plant, dynamic parameters such as vibration, impact, and pressure are the important factors used to determine its RUL. Therefore, based on the research results of the dynamic model, dynamic parameter soft sensor method, data-driven RUL prediction method, and other aspects, the RUL prediction method for special power plants can be converged to the RUL prediction method driven by a dynamic model, RUL prediction method driven by dynamic data-model fusion [26]. Based on the research results of the above three aspects, it is important to consider multiple conditions, multiple factors, and different combination structures of dynamic analog-fusion-driven parametric RUL prediction models and clarify the mapping relationship between dynamic parameters and the RUL of the reciprocating motion mechanism.