Design and Implementation of PHM System Framework for Unmanned Surface Vehicles

Zhou, Yuxiang; Hu, Tao; Yang, Jianjun

doi:10.1109/phm-qingdao46334.2019.8942844

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…This targeted approach allows for in-depth analysis of specific components, leading to the development of tailored health monitoring and prognostic techniques. Zhou et al 22 proposed a monitoring and health management system design for a radar klystron based on a multimethod modeling technique. This study emphasizes the need for tailored PHM solutions that account for the unique characteristics and failure modes of individual radar components.…”

Section: Phm In Radar Systemsmentioning

confidence: 99%

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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%

Section: Introductionmentioning

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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show abstract

“…Once these algorithms have been confirmed to successfully identify failure prior to occurrence, they can then be applied for further development to full operation of the system. At this point, the PHM system can focus on the ability to "achieve critical fault monitoring and early warning during the mission, based on real-time status data and pre-set rules" (Zhou, Hu, & Yang, 2019).…”

Section: Real-time Implementation Of Prognosticsmentioning

confidence: 99%

PHM Digital Hierarchy of Needs

Barta

Erlhoff²,

Alfeerawi³

2022

PHM_CONF

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In today’s modern world, with the abundance of digital data, data science is identified as a rigorous discipline and machine learning (ML) techniques are commonly used. The Prognostics & Health Management (PHM) field can successfully be executed utilizing a foundational approach where a digital hierarchy of needs is established for successful implementation of PHM on a large-scale system. This paper rationalizes the digital hierarchy of needs as it applies to PHM, explains how each foundational concept is essential, and builds upon the base-level concepts through analysis and implementation. First, this paper expounds how the integration of digital data from the lower-level components to the system level is critical for the success of PHM-enabled Systems. Once established, arguments will be presented for mapping the appropriate fault data to the corresponding components for the purpose of correlating failures to fault data. Subsequently, a case is presented for using real data to conduct Fault Detection, Fault Isolation, and simple prognostics analyses. Advanced PHM analyses can then be conducted utilizing data science and machine learning techniques with the intent of predictive maintenance analysis. Lastly, an argument is presented answering the need for an approach to implement real-time predictive activities once the complex analysis is validated and verified. This concept can be seen graphically in the figure below. Data integration is the foundation of the pyramid, supporting the identification of fault and parametric data, and followed by Fault Detection, Fault Isolation, and Simple Prognostics. The top tiers of the pyramid then can integrate complex analysis such as ML, and real-time implementation.

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Distributed Intelligent Health Status Assessment for More Electric Aircraft

Dong

Zhang

2022

2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)

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Design and Implementation of PHM System Framework for Unmanned Surface Vehicles

Cited by 3 publications

References 8 publications

Systematic prognostics framework development approach for a radar receiver

Systematic prognostics framework development approach for a radar receiver

PHM Digital Hierarchy of Needs

Distributed Intelligent Health Status Assessment for More Electric Aircraft

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