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
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  • Abstract
  • Description
  • Claims
  • Application Information

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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

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  • 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

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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...

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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

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Application Information

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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
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