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

A planetary gear and fault identification technology, which is applied in machine gear/transmission mechanism testing, machine/structural component testing, instruments, etc., can solve problems such as planetary gear fault identification, achieve strong anti-noise ability, good diagnostic effect, and prevent The effect of overfitting

Active Publication Date: 2018-12-21
哈尔滨科速智能科技有限公司
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  • Application Information

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Problems solved by technology

[0004] In order to solve the problem of planetary gear fault identification under noise environment and time-varying speed conditions, the present

Method used

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

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specific Embodiment approach 1

[0021] Specific Embodiment 1: This embodiment provides a planetary gear fault identification method based on a stacked denoising autoencoder and a gated recurrent unit neural network, such as figure 1 As shown, the specific implementation steps of the method are as follows:

[0022] Step 1. Construct a hybrid model based on SDAE and GRUNN, eliminate the noise component of the input data, process the time series data associated before and after, and automatically extract robust fault features from noisy samples;

[0023] Step 2, regard the training sample of planetary gear fault diagnosis as the input data of the hybrid model constructed in step 1, and use Adam optimization algorithm and dropout technology to train the hybrid model to prevent the occurrence of overfitting phenomenon;

[0024] Step 3. According to the trained hybrid model, use a softmax classifier to identify the state of the planetary gear in the sample to be diagnosed.

specific Embodiment approach 2

[0025] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that in said step 1, the hybrid model based on SDAE and GRUNN is composed of SDAE, GRUNN and softmax classifier, wherein: the input data of SDAE is planetary gear vibration The time-domain signal of the time-domain signal, the SDAE with multi-hidden layer structure can eliminate the noise component of the input signal. The output of SDAE is regarded as the input of GRUNN, so as to extract the fault features of the input signal. A softmax classifier converts the extracted fault features into a probability distribution of planetary gear states.

specific Embodiment approach 3

[0026] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is: in said step two, the concrete steps of adopting Adam optimization algorithm and dropout technology to train hybrid model are as follows:

[0027] Step 21, setting the noise ratio added to the SDAE input data, and realizing the initialization of each hidden layer parameter of SDAE by minimizing the reconstruction error of input and output;

[0028] Step 22. Set the dropout rate and apply the dropout technique to the hybrid model to obtain a "thinner" deep learning model;

[0029] Step two and three, calculate the cross-entropy loss function between the probability distribution of the softmax classifier output and the probability distribution of the target class, and use it as the objective function in the Adam optimization algorithm. The calculation formula of the cross-entropy loss function is:

[0030]

[0031] In the formula, p(x) is the probability distributio...

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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 ...

Claims

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Application Information

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IPC IPC(8): G01M13/02
CPCG01M13/021
Inventor 于军于广滨
Owner 哈尔滨科速智能科技有限公司
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