An integrated PHM framework for radar systems through system structural decomposition  

作　　者：WANG Hong KULEVOME Delanyo Kwame Bensah ZHAO Zi’an 

机构地区：[1]School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China

出　　处：《Journal of Systems Engineering and Electronics》2025年第1期95-107,共13页系统工程与电子技术(英文版)

基　　金：National Natural Science Foundation of China (42027805)。

摘　　要：Implementing an efficient real-time prognostics and health management (PHM) framework improves safety and reduces maintenance costs in complex engineering systems.However, research on PHM framework development for radar systems is limited. Furthermore, typical PHM approaches are centralized, do not scale well, and are challenging to implement.This paper proposes an integrated PHM framework for radar systems based on system structural decomposition to enhance reliability and support maintenance actions. The complexity challenge associated with implementing PHM at the system level is addressed by dividing the radar system into subsystems. Subsequently, optimal measurement point selection and sensor placement algorithms are formulated for effective data acquisition. Local modules are developed for each subsystem health assessment, fault diagnosis, and fault prediction without a centralized controller. Maintenance decisions are based on each local module’s fault diagnosis and prediction results. To further improve the effectiveness of the prognostics stage, the feasibility of integrating deep learning (DL) models is also investigated.Several experiments with different degradation patterns are performed to evaluate the effectiveness of the framework’s DLbased prognostics model. The proposed framework facilitates transitioning from traditional reactive maintenance practices to a predictive maintenance approach, thereby reducing downtime and improving the overall availability of radar systems.

关 键 词：deep learning prognostics and health management(PHM) radar systems remaining useful life(RUL) 

分 类 号：TN957.51[电子电信—信号与信息处理] TP18[电子电信—信息与通信工程]