To deal with the problems of low accuracy and poor generalization ability of high-power motor fault diagnosis in complex operation environment

a fault diagnosis method based on sound-vibration signal combined with one-dimensional convolutional neural network (1D-CNN) was proposed. First

a collected sound signal was denoised by the combination of background noise database and sparse representation. Next

the sound signal was filtered by band-pass filter (7-20 kHz)

and low frequency vibration signal (within 7 kHz) was superimposed to form more complete motor state representation information in frequency band. Then

the information after filtering and purification was expanded by overlapping data to obtain a large amount of data required for 1D-CNN training. Finally

data samples were input into 1D-CNN for learning and training. Local response normalization (LRN) and kernel function decorrelation were used to improve the structure of 1D-CNN model

which reduced the impact of positive and negative half-cycle fluctuations of pumping unit on motor diagnostic accuracy. Diagnostic results show that the overall diagnostic accuracy of CNN fault diagnosis reached 97.75% based on combined sound and vibration signal analysis

and the generalization ability was good. Compared with traditional motor fault diagnosis methods

the proposed method had obvious advantages.