Planetary gear fault identification method based on stacked denoising autoencoder and gated recurrent unit neural network

Abstract

The invention discloses a planetary gear fault identification method based on an SDAE and a GRUNN. The method comprises the following steps of step 1, constructing a mixed model based on the SDAE andthe GRUNN, and eliminating noise components of input data, processing the time sequence data which are related before and after, and automatically extracting robust fault features from noisy samples;step 2, taking the training samples of the fault diagnosis of the planetary gears as input data of the mixed model constructed in the step 1, training the mixed model through an Adam optimization algorithm and a dropout technology, and preventing the occurrence of an over-fitting phenomenon; and step 3, identifying the state of the planetary gears in the to-be-diagnosed samples through a softmax classifier according to the trained mixed model. According to the method, a good diagnosis effect can be obtained under the condition that the training sample number is small, so that the method has relatively high anti-noise capability and time-varying rotating speed adaptability, thereby providing a novel solving idea for the fault identification of the planetary gears.

Description

technical field

[0001] The present invention relates to a planetary gear fault identification method, in particular to a planetary gear fault identification method based on a stacked denoising autoencoder (Stacked denoising autoencoder, SDAE) and a gated recurrent unit neural network (Gated recurrent unit neural network, GRUNN) . Background technique

[0002] Planetary gearboxes have the characteristics of large transmission ratio and compact structure, and have been widely used in mechanical transmission systems of automobiles, wind power generation and helicopters. The complex and harsh working environment often leads to failures such as cracks, pitting and wear of the gears inside the planetary gearbox, which will cause the failure of the entire system and even lead to huge economic losses. Therefore, the fault diagnosis of planetary gearbox is of great significance to avoid potential accidents and ensure the reliable operation of mechanical systems.

[0003] In recent ...

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