A feature fused anti-noise intelligent fault diagnosis method of induction motor under variable conditions

Abstract

Under the conditions of variable speed and load for the motor, there are some problems such as complex operation data, difficult fault feature extraction and insufficient model generalization ability. A feature fusion method based on adaptive chirp mode decomposition (ACMD) and maximum absolute value rule (MAVR) and a fault diagnosis model based on improved convolutional neural network are proposed, which provides an efficient anti-noise fault diagnosis solution for the motor under variable conditions. First, the instantaneous frequencies (IFs) of vibration and current signals are extracted by ACMD, and then the MAVR is used to fuse the two IFs as new samples and input into the CNN model based on convolution kernel width and depth optimization. Based on a self-built platform, the experimental data of three-phase induction motor from static state to operation at 1800 rpm and from no load to heavy load under normal state, bearing and rotor mechanical faults, stator and rotor electrical faults are obtained, and the fault recognition accuracy of the proposed fault diagnosis method on the training set is more than 97%. In the model test, Gaussian white noise, colored noise, and random uniform distribution noise are added to the test set in a single and mixed way, respectively. The results show that the accuracy of the method is more than 71% when the noise intensity is greater than the signal strength, the accuracy is more than 84% when the noise intensity is equal to the signal strength, and the accuracy is more than 89% when the noise intensity is less than the signal strength, which proves that the proposed fault diagnosis method has strong anti-noise capability and robustness.