Multi-fault recognition of gear based on wavelet image fusion and deep neural network

Abstract

The coal mining environment where the plate conveyor is located often has narrow space, violent mechanical vibration, and explosion-proof requirements. Therefore, collecting vibration signals by installing sensors will have adverse problems such as difficult installation, strong noise, and potential safety hazards. In view of the weakness of the gear torsional load in the current signal, this paper proposes using three-phase current signal fusion to extract its phase difference information. At the same time, in order to extract the current information and phase information change caused by the early fault of the scraper conveyor gear, a gear fault diagnosis method based on the deep convolution neural network and three-phase current continuous wavelet image fusion is proposed. This method transforms the gear fault diagnosis problem into an image analysis problem. By fusing the time-frequency images of three-phase current, the phase difference information of the image can be obtained, and then the fluctuation state of motor torque can be determined. Then, the deep convolution neural network model is built to realize the fault feature recognition of the wavelet fusion image.