A New Method of Fault Detection and Location for Phased Array EMC Based on Deep Learning

The traditional fault detection and location method for phased array electromagnetic compatibility (EMC) needs a complex detection process with a larger number of data, which suffers from low efficiency with a long time for locating EMC faults. This paper presents a new method for fault detection and location of phased array EMC based on deep learning. It is used to extract the characteristics of induced current signals generated by interference sources on phased array antennas and fit the causalities between them. The proposed method enables rapid and precise detection and localization of faults, thereby presenting a novel approach to phased array EMC fault detection and location.