This study used the ZOA-CNN method to diagnose elevator bearing faults,aiming to further determine whether there are faults in the elevator by analyzing the bearing vibration signals during elevator operation.Convolutional neural network(CNN)has excellent automatic feature extraction ability,which provides strong support for elevator bearing fault diagnosis.Meantime,the paper combined the Zebra Optimization Algorithm(ZOA)to optimize the CNN model parameters to improve diagnostic performance.The research results show that this method has achieved significant results in diagnosing elevator bearing faults,with a diagnostic accuracy of 99.75%,which is significantly higher than the correct rate of traditional fault diagnosis methods for elevator faults.
elevator failureConvolutional Neural Network(CNN)Zebra Optimization Algorithm(ZOA)fault diagnosis
NETL
NSTL
万方数据
维普
