Study on the Health Condition Monitoring Method of Hydraulic Pump Based on Convolutional Neural Network

Aiming at the different health states of hydraulic pumps, this paper proposes to use the convolutional neural network to classify and identify the time-frequency images of different volumetric efficiency of hydraulic pumps to realize the health status monitoring of hydraulic pumps. After collecting the vibration signal data of the hydraulic pump, the time-frequency map is formed by short-time Fourier transform, wavelet transform, and Wigner-Will distribution, and then the generated time-frequency map is divided into a training set and a test set, in which the former is used to train the convolutional neural network and the latter is used to verify the recognition result. Finally, it is proved that the convolutional neural network can well identify the different health states of the hydraulic pump, and the recognition rate reaches 99%.