A Novel General Feature Enhancement Method Based on Genetic Programming for Improving RF Circuit Fault Diagnosis Using Machine Learning

Radio frequency (RF) circuits play a crucial role in numerous fields such as communication, radar, and navigation. However, due to their high operating frequencies, they are prone to failures under the influence of environmental factors and parasitic parameters. Existing methods for diagnosing RF ci...

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Veröffentlicht in:	Circuits, systems, and signal processing Jg. 44; H. 12; S. 8877 - 8900
Hauptverfasser:	Wu, Kunping, Long, Bing, Bu, Zhiyuan, Wang, Jingyuan, Liu, Zhen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.12.2025 Springer Nature B.V
Schlagworte:	Accuracy Circuits Circuits and Systems Classification Deep learning Electrical Engineering Electronics and Microelectronics Engineering Failure Fault diagnosis Genetic algorithms Instrumentation Machine learning Methods Neural networks Physical properties Radio frequency Signal,Image and Speech Processing Support vector machines Time-frequency analysis Fault diagnosis Feature enhancement Genetic programming (GP) Machine learning Radio frequency (RF) circuit
ISSN:	0278-081X, 1531-5878
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Abstract	Radio frequency (RF) circuits play a crucial role in numerous fields such as communication, radar, and navigation. However, due to their high operating frequencies, they are prone to failures under the influence of environmental factors and parasitic parameters. Existing methods for diagnosing RF circuit faults are mainly based on deep learning approaches. But the limited number of internal measurement points and the large variety of fault patterns result in complex network structure design and difficulties in application. In this manuscript, a novel general feature enhancement method based on genetic programming (GP) is proposed to improve the machine learning-based RF circuit fault diagnosis. Firstly, the time-frequency analysis of the fault signal is carried out based on the Variable Mode Decomposition-Hilbert (VMD-Hilbert) transform to obtain the original feature set. Then, the feature reconstruction method based on GP is used to achieve feature enhancement. Finally, the enhanced features are combined with machine learning algorithms to realize the fault diagnosis of RF circuits. Taking the experiment of a low-noise amplifier circuit as an example, after adopting the feature enhancement method in this manuscript, the diagnostic accuracies of Support Vector Machine and Naive Bayes are increased by 10.48% and 10.21% respectively. The experimental results demonstrate that this feature enhancement method can significantly improve the accuracy and stability of RF circuit fault diagnosis. Moreover, this feature enhancement method can be combined with any machine learning algorithm and applied to the fields of fault diagnosis, prediction, etc. of other systems.
AbstractList	Radio frequency (RF) circuits play a crucial role in numerous fields such as communication, radar, and navigation. However, due to their high operating frequencies, they are prone to failures under the influence of environmental factors and parasitic parameters. Existing methods for diagnosing RF circuit faults are mainly based on deep learning approaches. But the limited number of internal measurement points and the large variety of fault patterns result in complex network structure design and difficulties in application. In this manuscript, a novel general feature enhancement method based on genetic programming (GP) is proposed to improve the machine learning-based RF circuit fault diagnosis. Firstly, the time-frequency analysis of the fault signal is carried out based on the Variable Mode Decomposition-Hilbert (VMD-Hilbert) transform to obtain the original feature set. Then, the feature reconstruction method based on GP is used to achieve feature enhancement. Finally, the enhanced features are combined with machine learning algorithms to realize the fault diagnosis of RF circuits. Taking the experiment of a low-noise amplifier circuit as an example, after adopting the feature enhancement method in this manuscript, the diagnostic accuracies of Support Vector Machine and Naive Bayes are increased by 10.48% and 10.21% respectively. The experimental results demonstrate that this feature enhancement method can significantly improve the accuracy and stability of RF circuit fault diagnosis. Moreover, this feature enhancement method can be combined with any machine learning algorithm and applied to the fields of fault diagnosis, prediction, etc. of other systems.
Author	Wang, Jingyuan Wu, Kunping Liu, Zhen Long, Bing Bu, Zhiyuan
Author_xml	– sequence: 1 givenname: Kunping surname: Wu fullname: Wu, Kunping organization: School of Automation and Engineering, University of Electronic Science and Technology of China – sequence: 2 givenname: Bing orcidid: 0000-0003-1876-9013 surname: Long fullname: Long, Bing email: longbing@uestc.edu.cn organization: School of Automation and Engineering, University of Electronic Science and Technology of China – sequence: 3 givenname: Zhiyuan surname: Bu fullname: Bu, Zhiyuan organization: School of Automation and Engineering, University of Electronic Science and Technology of China – sequence: 4 givenname: Jingyuan surname: Wang fullname: Wang, Jingyuan organization: School of Automation and Engineering, University of Electronic Science and Technology of China – sequence: 5 givenname: Zhen surname: Liu fullname: Liu, Zhen organization: School of Automation and Engineering, University of Electronic Science and Technology of China
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SubjectTerms	Accuracy Circuits Circuits and Systems Classification Deep learning Electrical Engineering Electronics and Microelectronics Engineering Failure Fault diagnosis Genetic algorithms Instrumentation Machine learning Methods Neural networks Physical properties Radio frequency Signal,Image and Speech Processing Support vector machines Time-frequency analysis
Title	A Novel General Feature Enhancement Method Based on Genetic Programming for Improving RF Circuit Fault Diagnosis Using Machine Learning
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