Research on Control cubicle Fault Diagnosis Method Based on RFID Sensing and Deep Belief Network

To enhance the accuracy of control cabinet fault diagnosis, this paper proposes a control cabinet fault diagnosis algorithm based on RFID sensors and deep learning. Firstly, RFID sensor tags are designed to collect humidity and temperature signals from control cabinet. Secondly, the collected signals undergo deep feature extraction through Deep Belief Networks (DBN), and Sparse Code (SC) is integrated into the DBN network to improve its detection accuracy. Experimental results demonstrate that, compared to other algorithms, the SDBN-BP fault diagnosis model proposed in this paper exhibits higher detection accuracy, faster recognition speed, and an accuracy rate of up to $99.63 \%$.