Research on Hydraulic Power System Operation Status Diagnosis Technology Based on Hybrid CNN Model

Abstract

Aiming at the problems that the features extracted from the traditional system operation state are not adaptive and the specific system operation state is difficult to match, a gearbox system operation state diagnosis method based on continuous wavelet transform (CWT) and two-dimensional convolutional neural network (CNN) is proposed. The method uses the continuous wavelet transform to construct the time-frequency map of the hydrodynamic system operating state signal, and uses it as the input to construct a convolutional neural network model, and forms a deep distributed system operating state feature expression through a multilayer convolutional pool. The structural parameters of each layer of the network are adjusted by the back propagation algorithm to establish an accurate mapping from the signal characteristics to the system operating state. In the experiments under different working conditions and different system operation states, the accuracy of system operation state recognition reaches 99.2%, which verifies the effectiveness of the method. Using this method of adaptively learning rich information in the signal can provide a basis for intelligent system operation state diagnosis.