DocumentCode :
256632
Title :
A fault diagnosis method of communication connectors in wireless receiver front-end circuits
Author :
Rui Ji ; Jinchun Gao ; Gang Xie ; Flowers, G.T. ; Chen Chen
Author_Institution :
Sch. of Electron. Eng., Beijing Univ. of Posts & Telecommun., Beijing, China
fYear :
2014
fDate :
12-15 Oct. 2014
Firstpage :
1
Lastpage :
6
Abstract :
Electrical connectors play a critical role in wireless communication systems by serving as the infrastructure for exchanging information between the circuit modules. Unexpected connector failures can alter the signal that is being transmitted and lead to faults as well as bit errors. In this work, a fault detection and location method for connectors in wireless receiver front-end circuits was presented by simulation results. For connectors suffering from different levels of pollution, their impedance modulus varies continuously. In previous works, the parameters of the connectors or faulty components were usually assumed to be a certain specific value during fault diagnosis. In this current study, the impedance model of faulty connector and the range of the connector´s impedance parameters are considered in order to better match the real system. First, the fault modes were determined. The parameters of the connectors were then sampled to generate training and testing samples. Finally, based on the samples, a MMSE (Minimum Mean Square Error) algorithm and a neural network algorithm are adopted to classify the fault modes. Both algorithms can diagnose the faulty connectors in a wireless receiver front-end circuit. If trained and tested by data obtained from actual wireless receiver circuits, this method can be applied to diagnose the faulty connectors in an actual circuit. In addition, this fault diagnosis method of connectors is completely automated and can also be applied to other types of circuits.
Keywords :
electric connectors; fault location; learning (artificial intelligence); least mean squares methods; neural nets; radio receivers; telecommunication computing; telecommunication network reliability; MMSE algorithm; communication connector; electrical connector; fault detection method; fault diagnosis method; fault location method; faulty connector model; impedance modulus; information exchange; minimum mean square error algorithm; neural network algorithm; training sample; wireless communication system; wireless receiver front-end circuit; Circuit faults; Classification algorithms; Connectors; Contacts; Receivers; Resistance; Wireless communication; MMSE; Neural Network; communication connector; fault diagnosis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Contacts (Holm), 2014 IEEE 60th Holm Conference on
Conference_Location :
New Orleans, LA
Type :
conf
DOI :
10.1109/HOLM.2014.7031061
Filename :
7031061
Link To Document :
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