Hydraulic valve fault diagnosis method based on dilated convolutional neural network

Abstract

The invention relates to the technical field of fault diagnosis, in particular to a hydraulic valve fault diagnosis method based on a dilated convolutional neural network, which comprises the steps of using three groups of heterogeneous sensors to collect fault data of a hydraulic reversing valve, and each group of heterogeneous sensors comprises two homogeneous sensors; carrying out segmentation and polar coordinate transformation on the fault data acquired by each sensor, and converting the fault data into an image; performing redundancy processing on each image, and fusing the images of the two homogeneous sensors; constructing a dilated convolutional neural network model, and training the image through the dilated convolutional neural network model; adjusting parameters and a structure of the dilated convolutional neural network model according to the accuracy of fault classification to obtain a dilated convolutional neural network model with an optimal local network structure; and performing fault diagnosis on the hydraulic reversing valve by using the trained dilated convolutional neural network model. According to the method, the problem of fault detection by a single sensor is solved, and the accuracy and robustness of fault diagnosis are improved.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a hydraulic valve fault diagnosis method based on a hollow convolutional neural network. Background technique [0002] As one of the important components of the hydraulic system, the hydraulic valve has been widely used in industrial production equipment and aerospace equipment. With the continuous development of the hydraulic system towards intelligence, the requirements for self-diagnosis of faults of various components in the hydraulic system are getting higher and higher, and the backward self-testing ability of the existing hydraulic valves has seriously restricted the intelligent development of the hydraulic system. And the hydraulic valve is an important control element in the hydraulic system, ensuring its normal operation is the focus of protecting the normal operation of the entire hydraulic system, so it is very valuable and practical to monitor the working sta...

AI Technical Summary

Problems solved by technology

[0007] The present invention intends to provide a hydraulic valve fault diagnosis method based on a hollow convolutional neural network that can fuse data collected by multiple sensors and accurately monitor the fault of a hydraulic reversing valve, so as to overcome the problem of fault detection by a single sensor, Improve the accuracy and robustness of fault diagnosis

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