Intelligent Detection Methods of Electrical Connection Faults in RF Circuits

Printed circuit boards (PCBs) have a large number of electrical connection nodes. Exposure to harsh environments may lead to connection faults in these nodes. In the present work, intelligent detection methods for electrical connection faults were studied. Specifically, the fault characteristics of...

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Autores principales:	Ziren Wang, Jiaqi Li, George T. Flowers, Jinchun Gao, Kaixuan Song, Wei Yi, Zhongyang Cheng
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	electrical connection fault intelligent detection signal integrity Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/5a5049d0f1ce49f2b16f35ac8f513d77
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spelling	oai:doaj.org-article:5a5049d0f1ce49f2b16f35ac8f513d772021-11-11T15:03:23ZIntelligent Detection Methods of Electrical Connection Faults in RF Circuits10.3390/app112199732076-3417https://doaj.org/article/5a5049d0f1ce49f2b16f35ac8f513d772021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9973https://doaj.org/toc/2076-3417Printed circuit boards (PCBs) have a large number of electrical connection nodes. Exposure to harsh environments may lead to connection faults in these nodes. In the present work, intelligent detection methods for electrical connection faults were studied. Specifically, the fault characteristics of connectors, bonding wires and solder balls in the frequency domain were analyzed. The reflection and transmission parameters of an example filter circuit with electrical connection faults were calculated using the Simulation Program with Integrated Circuit Emphasis (SPICE). With these obtained electrical parameters, three machine learning algorithms were used to detect example electrical connection faults for the example circuit. Based upon the performance evaluations of the three algorithms, one can conclude that machine-learning-based intelligent fault detection is a promising technique in diagnosing circuit faults due to electrical connection issues with high accuracy and lower time cost as compared to current manual processes.Ziren WangJiaqi LiGeorge T. FlowersJinchun GaoKaixuan SongWei YiZhongyang ChengMDPI AGarticleelectrical connection faultintelligent detectionsignal integrityTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9973, p 9973 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	electrical connection fault intelligent detection signal integrity Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	electrical connection fault intelligent detection signal integrity Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Ziren Wang Jiaqi Li George T. Flowers Jinchun Gao Kaixuan Song Wei Yi Zhongyang Cheng Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
description	Printed circuit boards (PCBs) have a large number of electrical connection nodes. Exposure to harsh environments may lead to connection faults in these nodes. In the present work, intelligent detection methods for electrical connection faults were studied. Specifically, the fault characteristics of connectors, bonding wires and solder balls in the frequency domain were analyzed. The reflection and transmission parameters of an example filter circuit with electrical connection faults were calculated using the Simulation Program with Integrated Circuit Emphasis (SPICE). With these obtained electrical parameters, three machine learning algorithms were used to detect example electrical connection faults for the example circuit. Based upon the performance evaluations of the three algorithms, one can conclude that machine-learning-based intelligent fault detection is a promising technique in diagnosing circuit faults due to electrical connection issues with high accuracy and lower time cost as compared to current manual processes.
format	article
author	Ziren Wang Jiaqi Li George T. Flowers Jinchun Gao Kaixuan Song Wei Yi Zhongyang Cheng
author_facet	Ziren Wang Jiaqi Li George T. Flowers Jinchun Gao Kaixuan Song Wei Yi Zhongyang Cheng
author_sort	Ziren Wang
title	Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
title_short	Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
title_full	Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
title_fullStr	Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
title_full_unstemmed	Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
title_sort	intelligent detection methods of electrical connection faults in rf circuits
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5a5049d0f1ce49f2b16f35ac8f513d77
work_keys_str_mv	AT zirenwang intelligentdetectionmethodsofelectricalconnectionfaultsinrfcircuits AT jiaqili intelligentdetectionmethodsofelectricalconnectionfaultsinrfcircuits AT georgetflowers intelligentdetectionmethodsofelectricalconnectionfaultsinrfcircuits AT jinchungao intelligentdetectionmethodsofelectricalconnectionfaultsinrfcircuits AT kaixuansong intelligentdetectionmethodsofelectricalconnectionfaultsinrfcircuits AT weiyi intelligentdetectionmethodsofelectricalconnectionfaultsinrfcircuits AT zhongyangcheng intelligentdetectionmethodsofelectricalconnectionfaultsinrfcircuits
_version_	1718437141056323584

Intelligent Detection Methods of Electrical Connection Faults in RF Circuits

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