A fault diagnosis method of communication connectors in wireless receiver front-end circuits

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.