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A New Convolutional Neural Network Model and Its Application

A convolutional neural network and model technology, applied in the new convolutional neural network model and application field, can solve the problems of low diagnostic accuracy and difficulty, and achieve good classification ability

Active Publication Date: 2021-10-19
TAIYUAN FORTUCKY LOGISTICS EQUIP TECH CO LTD +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of the deficiencies in the prior art, the present invention solves the problem of low diagnostic accuracy caused by gradient disappearance by improving the activation function linear correction unit; two layers of residual neuron layers are added to the convolutional neural network to deepen Network depth to facilitate the extraction of potential features that are not easy to be explored; thereby constructing a new convolutional neural network model and applying it to bearing fault diagnosis

Method used

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  • A New Convolutional Neural Network Model and Its Application
  • A New Convolutional Neural Network Model and Its Application
  • A New Convolutional Neural Network Model and Its Application

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0050] like Figure 1-3 As shown, a new type of convolutional neural network model is based on convolutional neural network as a model based on the modified threshold function to activate the function Trelu, introduced in the intermediate layer of the convolutional neural network to the residual neuron, convolution and the pool. Chemical interplace, Softmax classification, generating new convolutional neural network model rlcnn throughout the structure; the activation function Trelu is shown in formula:

[0051]

[0052] The residual neuron is represented by formula (2):

[0053] F (x) = w 2 f 1 X + b) + b (2)

[0054] The formula (2) indicates the input of the current layer, and f (x) indicates the input of the next layer, W 1 W 2 The weight of the current layer and the next layer, F (.) Indicates the Trelu activation function, and B represents the bias.

[0055] The new convolutional neural network model is a six-layer convolutional neural network, of which the first, second, f...

Embodiment 2

[0064] The motor bearing fault is diagnosed using the new convolutional neural network model of the present invention, and the accuracy of the RLCNN model of the present invention is verified that the basic flow of the RLCNN model fault diagnosis is Figure 4 Indicated.

[0065] 1, data set

[0066] The RLCNN model of the present invention is verified using the bearing data set of Case Seminary. Three types, outer ring failure (OF), rolling body failure (RF), inner ring failure (IF), 3 damage to each type, damaged diameter 0.18mm, 0.36mm, 0.54mm, total 9 Types, add a normal (NO) type, use IF0.18, IF0.36, IF0.54, OF0.18, OF RF0.36, RF0.18, RF0.36, RF0 .54 9 types of fault types, NOs are normal. These data are recorded under the four load conditions (0HP, 1HP, 2HP, 3HP), in the training data set, each load has 2000 vibrating images; in the test data set, each load has 400 vibration images, images The size is 64 × 64. They vibrate image samples such as Figure 5 . From Figure 5 It can ...

Embodiment 3

[0075] Using the new convolutional neural network model of the present invention, the electromechanical transmission system bearing fault is diagnosed, and the accuracy of the RLCNN model of the present invention is verified that the accuracy of the bearing fault diagnosis is verified, and the RLCNN model fault diagnosis is based. Figure 4 Indicated.

[0076] 1, data set

[0077] The use of bearing data sets provided by the University of Padel Bern, Germany analyzes the application of the RLCNN model of the present invention in a bearing fault diagnosis. Select a part of the data set for training and testing, some people in the data set are damaged and true damage (generated by experiments in the service life), select 5 damage to the inner ring in real injuries, pumping multiple damage level 1 (ki04), Pixabavi-single damage 3 (Ki16), dumping repeated injury level 1 (ki17), pumping a single damage level 2 (Ki18), pumping a single damage 1 (Ki21) and normal (NO) Total 6 fault types....

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Abstract

The invention discloses a new convolutional neural network model and its application. The model is based on the convolutional neural network, uses an improved threshold function as the activation function tReLU, and introduces residual neurons into the middle layer of the convolutional neural network. , use convolution and pooling to alternately connect, softmax classification, and generate a novel convolutional neural network model RLCNN throughout the entire structure; the activation function tReLU is shown in formula (1). The method of using this model for bearing fault diagnosis is as follows: firstly, the vibration signal from the bearing is converted into a two-dimensional vibration image, and the image is processed to obtain the pixel intensity matrix of the gray image, and the pixel intensity matrix is ​​input into the new convolutional neural network model, Take the existing public bearing data set as the training set, perform convolution and batch normalization processing; obtain the diagnosis result of the bearing fault. The invention solves the problem of low accuracy of bearing fault diagnosis caused by model gradient disappearance and mean value shift caused by traditional activation function.

Description

Technical field [0001] The present invention belongs to the technical field of computer convolutional neural network, and specific relates to a new type of convolutional neural network model and application. Background technique [0002] Modern machinery equipment is being carried out under complex and high-intensity work conditions. Once failed, it may be catastrophic, so mechanical failure hidden huge risks and economic losses. Bearings are important components of mechanical equipment, in order to make some equipment can function properly, the diagnosis of bearings is essential, and 30% is statistically caused by the damage of the bearing. Now the machinery equipment fault data is quite the "big data" era. [0003] How to automatically conduct feature mining in massive data, in order to replace artificial extraction, real-time detection of the bearing to ensure the accuracy and efficiency of fault diagnosis. Therefore, the intelligent diagnosis of the processing industry "big d...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/082G06N3/045
Inventor 许习军王巧文董增寿康琳刘鑫
Owner TAIYUAN FORTUCKY LOGISTICS EQUIP TECH CO LTD
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