Motor fault diagnosis method and system based on GRU network stator current analysis

Abstract

The invention provides a motor fault diagnosis method based on GRU network stator current analysis. The method comprises the steps of collecting stator current variable data; wirelessly transmitting and storing the acquired stator current variable data to a cloud database; preprocessing collected stator current variable data sets, and dividing all the data sets into multiple groups of training, verification and test sets; in the working state of a three-phase induction motor, modeling the acquired stator current variable data by using a GRU neural network; updating GRU model parameters by adopting an Adam adaptive learning rate method; carrying out sequence division on training set data according to GRU input dimensions, sending data of each sampling point into a GRU unit, carrying out feature extraction by the GRU, starting to train the model, and testing a verification set to further debug GRU model hyper-parameters; taking the features extracted by the GRU as the input of a full connection layer; and performing final current signal fault diagnosis by using the data of the test set.

Description

technical field [0001] The invention relates to the technical field of deep learning, in particular to a motor fault diagnosis method and system based on GRU network stator current analysis. Background technique [0002] Induction motors are widely used in the fields of industrial production, agricultural production, transportation, and national defense technology. Real-time fault diagnosis of motors can not only ensure the normal operation of the motors, but also detect problems and repair them in time to avoid unnecessary losses. [0003] At present, the feature extraction of most induction motor fault diagnosis methods is manual, such as determining the number of layers of the neural network, selecting the SVM core, etc. This method is not only time-consuming but also difficult to adapt to new working conditions. Contents of the invention [0004] (1) Solved technical problems [0005] Aiming at the deficiencies of the prior art, the present invention provides a motor ...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the deficiencies of the prior art, the present invention provides a motor fault diagnosis method and system based on GRU network stator current analysis, which solves the problem that the feature extraction of most current induction motor fault diagnosis methods proposed in the background technology is manual The method, such as determining the number of layers of the neural network, selecting the SVM core, etc., is not only time-consuming but also difficult to adapt to the new working environment.

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