Research on Power Supply Performance Degradation Based on Neighborhood Rough Set and Multiple Linear Regression

Li, Qinglan; Lv, Yongle; Zhou, Can; Qu, Hao

doi:10.1109/phm-qingdao46334.2019.8943065

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Similarly, Li et al 26 explored PHM for power supply subsystems, and Wang et al 27 investigated the application of PHM to cooling systems. A Bayesian network diagnosis method based on multisource information fusion technology is proposed by Ref.…”

Section: Phm In Radar Systemsmentioning

confidence: 99%

Systematic prognostics framework development approach for a radar receiver

Kulevome,

Wang,

Zhao

et al. 2024

J. Appl. Rem. Sens.

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Radar receivers are vital components in modern radar systems, and their reliable operation is crucial for accurate target detection and tracking. However, degrading receiver components can lead to reduced gain, increased noise levels, and decreased probability of detection affecting the overall radar performance. We present an efficient real-time prognostic framework for a radar receiver. The effect of the performance degradation of critical devices on the radar receiver is analyzed. A prognostic framework is developed based on the relationship between device health and receiver performance. Subsequently, an improved prognostic model based on the integration of Weibull distribution and long short-term memory network is developed and trained to accurately estimate the remaining useful life (RUL) of the receiver. Integrating survival analysis and deep learning techniques offers a robust solution for accurate RUL estimation, which can significantly enhance maintenance strategies. 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 receivers.

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Section: Phm In Radar Systemsmentioning

confidence: 99%

Systematic prognostics framework development approach for a radar receiver

Kulevome,

Wang,

Zhao

et al. 2024

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

show abstract

“…The implementation of PHM in radar systems is relatively new and challenging due to system complexities [20]. Most researchers have identified the transmitter [21], power supply [22], cooling system [23], and other electronic parts [24,25] as critical for diagnosis and prognosis. As an essential device of APARs, changes in the working parameters of the transmit/receive (T/R) modules directly affect the operation reliability.…”

Section: Phm Implementation In Radar Systemsmentioning

confidence: 99%

A Hierarchical PHM Framework for Phased Array Radar Systems

Kulevome,

Wang,

Zhao

et al. 2023

PIER M

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Phased array radar (PAR) systems are critical for modern defense and surveillance applications, but their reliability and availability are affected by various factors, including physical and performance degradation. Furthermore, implementing prognostics and health management (PHM) framework for the whole radar system is challenging. To address these issues, this paper proposes an efficient solution by hierarchically implementing PHM frameworks in an active PAR (APAR) system. The proposed framework subsumes device-level, subsystem-level, and system-level health prediction models to enable comprehensive health monitoring and maintenance decision-making. This approach addresses the unique challenges involved in implementing PHM for the APAR system and facilitates the transition from traditional reactive maintenance practices to a predictive maintenance approach, thereby improving the overall system. Mathematical models that relate the radar's physical degradation to its performance deterioration are formulated, analyzed, and presented. Subsequently, a Bayesian long short-term memory (BayesLSTM) architecture is developed and integrated into the proposed framework for estimating the remaining useful life (RUL) of critical devices/subsystems. The effectiveness of the proposed deep learning-based prognostic framework is evaluated through simulations and experimental studies. The proposed hierarchical framework has the potential to be applied to other radar systems that require effective health monitoring strategy.

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Research on Power Supply Performance Degradation Based on Neighborhood Rough Set and Multiple Linear Regression

Cited by 2 publications

References 9 publications

Systematic prognostics framework development approach for a radar receiver

Systematic prognostics framework development approach for a radar receiver

A Hierarchical PHM Framework for Phased Array Radar Systems

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