In order to improve the accuracy of switchgear fault detection, this paper proposes a fault detection algorithm for switchgear based on RFID sensors and deep learning. Firstly, RFID sensing tags are designed to collect the current signals and temperature of the switchgear. Secondly, the collected signals are subjected to deep-level feature extraction through a deep belief network (DBN), and sparse coding (SC) is integrated into the DBN to improve its detection accuracy. Finally, in order to improve the detection speed, an extreme learning machine (ELM) is used to classify and recognize the signals extracted from the features. The experimental results show that compared to other algorithms, the sparse DBN-ELM (SDBN–ELM) fault detection model proposed in the paper offers higher detection accuracy, faster recognition speed, and an accuracy rate of 99.63%.