Mechanical equipment health state identification method based on embedded cycle network

Abstract

The invention discloses a mechanical equipment health state identification method based on an embedded cycle network. The mechanical equipment health state identification method based on the embeddedcycle network comprises the following steps: firstly, obtaining a vibration signal sample of mechanical equipment and dividing the vibration signal sample into a plurality of sections of adjacent local vibration sub-signals; then extracting a time-frequency image from the sub-signals, and taking the time-frequency image as input after standardized processing; then constructing a depth residual network and training the depth residual network so as to learn various pieces of local information in the signals; and finally, embedding the trained residual network into an input layer of the cycle network by utilizing thought of migration learning, fixing a convolution part so that the convolution part does not participate in training, and re-training the embedded network by utilizing the originalsample input again so as to learn information between adjacent periods; and diagnosing the health state of the mechanical equipment by the trained embedded cycle network. According to the method, thelocal vibration characteristic and the relation between adjacent parts are modeled, the influence of noise and local interference in the signals on a classification effect is weakened, and the applicability of the network in practical engineering is improved.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of mechanical equipment, and in particular relates to a method for identifying the health status of mechanical equipment based on an embedded loop network, which considers the relationship between signal parts. Background technique [0002] With the development and progress of science and technology, mechanical equipment and its intelligent applications are more and more widely used in social production and life. Its health status is not only related to economic benefits, but also related to the safety of people and the whole society. However, the life of mechanical equipment is discrete, even if it comes from the same material, processing equipment and processing technology, its life will show huge differences. Therefore, it is necessary to monitor and diagnose the health status. Among the many existing diagnostic methods, noise is one of the key factors affecting the diagnostic accurac...

AI Technical Summary

Problems solved by technology

However, even if the deep residual network has good information mining ability, the inevitable noise and interference in the signal will affect the classification of the overall signal, making the existing deep residual network method's diagnostic ability in the environment of low signal-to-noise ratio weaker

Due to the lack of mining of information in the signal, the ability of the cyclic network in the diagnosis of mechanical equipment is weakened.

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