Research on fault diagnosis and analysis of AC relays based on CNN

Relay detection is an important step to ensure the reliable operation of relays in electrical systems. This paper proposes a fault diagnosis system for alternating current(AC) relays based on Convolutional Neural Networks(CNN). The system uses a vibration sensor to collect signals from the AC relay, and then transmits the vibration signals to the edge processing unit embedded with CNN. The edge processing unit extracts the time domain features of the AC relay vibration signal, and then classifies the features in the CNN model. Finally, the system outputs the results of abnormal vibration detection. The experimental results show that the system proposed in this paper can effectively identify the abnormal vibration of AC relays, reaching an accuracy rate of 98.3%, which is better than the traditional Support Vector Machines(SVM) or Random Forest(RF) methods. This paper provides a standardized, automated and intelligent solution for the field of abnormal vibration detection of AC relays, which helps to improve product quality and production efficiency.