Probability fatigue reliability evaluation method for high-speed rail bearing

A reliability and bearing technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve problems such as large bearing life error and manufacturing error

Active Publication Date: 2020-05-15
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

However, due to manufacturing errors, dispersion of material properties and randomness of loads, its fati...

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  • Probability fatigue reliability evaluation method for high-speed rail bearing
  • Probability fatigue reliability evaluation method for high-speed rail bearing
  • Probability fatigue reliability evaluation method for high-speed rail bearing

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[0055] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0056] In order to improve the accumulative evaluation accuracy of fatigue damage, the dispersion of material properties and the randomness of loads should be considered. In the absence of sufficient data to describe the dispersion of materials, the elastic modulus of materials is described by intervals, and the random working conditions of bearings are described by random variables. How to choose experimental design points under mixed uncertainties is a key issue. This scheme proposes a probabilistic fatigue life assessment method for high-speed rail bearings under mixed uncertainties. In the met...

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Abstract

The invention discloses a probability fatigue reliability evaluation method for a high-speed rail bearing, and the method comprises the steps: firstly carrying out the quantification of the dispersibility of a material and the randomness of a load for the complex correction of an L10 life formula, and selecting a random factor representative point based on a number theory point selection method; secondly, establishing a three-dimensional finite element simulation model of the high-speed rail bearing according to the selected representative points, further analyzing the contact stress of the bearing under the influence of random factors, and establishing a BP neural network agent model of the maximum contact stress; compiling an S-N curve of the material through a load spectrum; a finite element simulation method is used for analyzing the fatigue life of the bearing, the accumulated damage of the bearing and the reliability smaller than the critical damage are further calculated according to the fatigue damage accumulation principle, so that an effective method is provided for high-precision and high-efficiency depiction of failure physics and fatigue life evaluation of the high-speed rail bearing.

Description

technical field [0001] The invention belongs to the field of reliability of mechanical parts, and in particular relates to a method for evaluating the reliability of high-speed rail bearing probabilistic fatigue. Background technique [0002] At present, China has become the country with the highest mileage, speed and traffic volume of high-speed rail in the world. As the basic key spare parts of high-speed rail, the localization of bearings is a key issue that must be solved in my country's high-speed rail technology. Bearings of high-speed rail main gearboxes are subjected to high speed, random loads, and strong impacts during operation, and the failure physics is complex. L in the international standard ISO "Rated Dynamic Load and Rated Life of Rolling Bearings" 10 Life is a test and calculation carried out under specified conditions. In the specific operation of the bearing, due to the influence of factors such as random eccentric load, improper installation, and disp...

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Application Information

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IPC IPC(8): G06F30/17G06F30/23G06F30/27G06N3/04G06N3/08G06F119/02
CPCG06N3/084G06N3/044
Inventor 张小玲刘嘉庆张鸿鑫凌丹
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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