# A Performance Prediction Method Utilizing Time-Dependent Subsystem Transfers between Family Systems
The accurate prediction of equipment health is essential for ensuring optimal system functionality, minimizing maintenance costs, and enhancing equipment performance. There are two primary approaches to equipment health prediction, as follows: those based on physical failure models and those based on data-driven methods [7]. [PERSON] et al. employed statistical pattern recognition techniques to assess equipment performance and utilized an autoregressive moving average model for predicting device health status [8]. [PERSON] et al. applied LIB-SVM to effectively fit data with limited samples and non-linear conditions, enabling fault prediction for electronic devices [9]. Based on the theory of conditional probability, [PERSON] et al. proposed a method for calculating elasticity that incorporates false alarms and applied it to enhance the accuracy of health assessment for electro-hydrostatic actuators (EHAs) [10]. They probabilistically formulated a novel measure of elasticity that considers both the false alarm rate and reliability [11]. In comparison with conventional measures of elasticity, this newly developed measure enables a more rigorous and accurate estimation of system elasticity [12; 13]. The existing theoretical research on the thermal characteristics of electro-hydrostatic actuators (EHAs) primarily relies on a one-dimensional thermodynamic model established using the nodal method, which overlooks the comprehensive influence of motor heat generation on the overall EHA's characteristics [14; 15]. To address this limitation, [PERSON] et al. developed a simplified thermal characteristic analysis model for electro-hydraulic actuators [16], offering a solution approach for relevant simulation parameters and a theoretical framework to solve the overall temperature distribution. This model comprehensively con siders the impact of motors on an EHA's thermal characteristics and enhances its structure and performance through optimization. A structural optimization scheme was proposed based on comparisons between simulations and experiments [17; 18]. Most of the aforementioned prediction methods are based on conventional machine learning techniques. However, deep learning, being a data-driven algorithm, combines low-level features to form more abstract high-level features that can effectively capture the distributed characteristics of data. HEIMES employs recursive neural network methods for estimating the remaining useful life of systems [19]. [PERSON] et al. utilized autoencoders to monitor signal features and construct a deep neural network model for the time-series prediction of equipment health indicators [20]. [PERSON] et al. employed a combination neural network approach to identify the wear state of aircraft electro-hydrostatic actuator nozzle flapper valves [21; 22] in order to achieve health status prediction [23], which represents an innovative application based on model-based fault detection and identification (FDI) methods [24], by utilizing artificial neural networks for identifying the actual wear states of actuators [25; 26]. Currently, the majority of data-driven algorithms for life prediction solely focus on modeling system degradation data. However, with technological advancements, it has become feasible to acquire both degradation data and a substantial amount of monitoring data regarding the operating environment of systems in engineering practice. With ample support from such data, [PERSON] et al. have conducted research on the application and development of covariate [27] data derived from system operating environments in reliability assessment models [28]. They have identified limitations in current research methods and proposed corresponding solutions. The aforementioned research methodology requires a substantial amount of data support. During the data processing phase, it is typically assumed that the multiple datasets are independent from one another.