An integrated PHM framework for radar systems through system structural decomposition

doi:10.23919/JSEE.2024.000087

Abstract

Abstract:

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 DL-based 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.

Key words: deep learning, prognostics and health management (PHM), radar systems, remaining useful life (RUL)

Hong WANG, Delanyo Kwame Bensah KULEVOME, Zi’an ZHAO. An integrated PHM framework for radar systems through system structural decomposition[J]. Journal of Systems Engineering and Electronics, 2025, 36(1): 95-107.

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Data	t_p	Actual RUL	Predicted RUL	Error	R²	RMSE	MAPE
Sensor 1	600	254	251	3	0.9992	0.00615	0.6703
Sensor 1	750	104	102	2	0.9982	0.00555	0.5723
Sensor 2	600	323	306	17	0.9931	0.02301	2.4821
Sensor 2	750	173	169	4	0.9953	0.01014	1.2915
Sensor 3	600	233	236	3	0.9973	0.00342	2.7553
Sensor 3	750	83	81	2	0.9979	0.00293	2.5715
Sensor 4	600	386	394	8	0.9939	0.02223	2.7007
Sensor 4	750	236	239	3	0.9978	0.01250	2.1559

Data	t_p	Actual RUL	Predicted RUL	Error	R²	RMSE	MAPE
Sensor 1	600	254	248	6	0.9080	0.05347	3.80328
Sensor 1	750	104	106	2	0.9959	0.00723	0.69395
Sensor 2	600	323	315	8	0.9942	0.01356	1.90885
Sensor 2	750	173	170	3	0.9956	0.00935	0.88095
Sensor 3	600	233	229	4	0.9944	0.00850	1.1645
Sensor 3	750	83	83	0	0.9957	0.00317	0.3049
Sensor 4	600	386	381	5	0.9957	0.01881	1.6104
Sensor 4	750	236	234	2	0.9967	0.00680	0.5897

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