Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Loader drive axle extreme small sample reliability evaluation method based on BP neural network

A BP neural network, loader technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve problems such as inapplicability to very small samples

Active Publication Date: 2019-09-20
CHANGAN UNIV
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a method for evaluating the reliability of a very small sample of a loader drive axle based on a BP neural network, which solves the problem that the existing small sample test evaluation is not suitable for the test evaluation of a very small sample

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Loader drive axle extreme small sample reliability evaluation method based on BP neural network
  • Loader drive axle extreme small sample reliability evaluation method based on BP neural network
  • Loader drive axle extreme small sample reliability evaluation method based on BP neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0086] A method for evaluating the reliability of a loader drive axle provided by the invention operates on a Matlab platform and comprises the following steps:

[0087] Step 1, measure the fatigue life experimental data of n loader drive axle samples respectively by test, obtain n experimental data;

[0088] Step 2, establish the BP neural network model by the n test data obtained in step 1:

[0089] S1, according to the n fatigue life test data values ​​obtained in step 1 to form the original sequence t 1 ,t 2 ,...t n , and use the reliability formula (1) to calculate the reliability R(t 1 ),R(t 2 ),...R(t n ), the resulting reliability R(t 1 ),R(t 2 ),...R(t n ) as input for training BP neural network:

[0090]

[0091] Among them, u Y is the average life of the loader drive axle fatigue test sample; σ is the standard deviation of the loader drive axle sample, and its value is σ=0.17;

[0092] S2, make n fatigue life test data values ​​into the original sequen...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides a loader drive axle extreme small sample reliability evaluation method based on a BP neural network, and the method comprises the following steps: 1, respectively carrying out the fatigue life test of n loader drive axle samples, and obtaining n fatigue life test data values; 2, establishing a BP neural network model according to the n fatigue life test data values obtained in the step 1, and expanding the n fatigue life test data values obtained in the step 1 to m + n sample data through a BP neural network to obtain sample data X; 3, calculating a life average value uY' and a standard deviation [sigma] 'of the sample data X obtained in the step 2; 4, solving a lower limit value uY' min of the service life average value uY of the sample data corresponding to the 75% confidence according to the service life average value uY' of the sample data X obtained in the step 3 and the standard deviation [sigma] '; 5, calculating to obtain the reliability index of the loader drive axle according to the lower limit value uY' min obtained in the step 4. The loader drive axle reliability evaluation method is also suitable for reliability analysis of other mechanical products which are not easy to obtain a large number of samples.

Description

technical field [0001] The invention belongs to the technical field of reliability evaluation of mechanical products, and in particular relates to a method for evaluating the reliability of a very small sample of a drive axle of a loader based on a BP neural network. Background technique [0002] Loader belongs to the category of shovel transport machinery, is a very versatile construction machinery, can be used for loading and unloading, handling, leveling materials and light shoveling operations, widely used in construction, mines, highways, railways, hydropower, national defense projects and urban infrastructure. [0003] When the loader is working: the drive axle further increases the torque transmitted from the output shaft of the transmission, and further reduces the speed to overcome the resistance of the wheels, and at the same time transfers the input power to the wheels by changing the direction of 90°; the drive axle solves the problem through the differential. T...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06N3/08
CPCG06N3/084G06F30/20
Inventor 曹蕾蕾樊浩郭城臣郭磊古玉锋白杰王方方
Owner CHANGAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com