Fault diagnosis of motor bearing based on Mel-scale frequency cepstral coefficients and 1D-convolutional neural network

To address the problem with inadequate precision for motor rolling bearing faults identification, a method for diagnosing faults of motor bearing based on MFCC-1D-CNN was presented. The method combines the advantages of Mel-scale frequency cepstral coefficients (MFCC), which has a strong ability to extract features of spectral energy distribution, and one-dimensional convolutional neural network (1D⁃CNN), which has more lightweight structure. First, the acceleration signal from the acceleration sensor placed at the motor bearing was acquired. Then, the MFCC features were extracted from the acceleration signal. Finally, the features of MFCC were flattened and input into the 1D-CNN model, which can quickly recognize faults of motor bearing. Results of experiment express that the accuracy of this method for diagnosing the bearing faults of the motor can reach 99.94 %.