Gear contact fatigue life prediction method based on GA-BP neural network

A BP neural network and GA-BP technology, applied in the field of intelligent manufacturing, can solve the problems of imperfect accumulation theory of fatigue damage, failure to consider the influence of fatigue damage, fatigue damage of dangerous points, etc. The effect of high prediction accuracy

Pending Publication Date: 2020-06-23
CHONGQING UNIV
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Problems solved by technology

[0003] Existing gear fatigue life prediction methods mainly include fatigue cumulative damage theory, mathematical models, etc. The fatigue cumulative damage theory believes that when a part acts on a constant stress amplitude, its maximum life is N times, that is, when the part is running N times, its danger point will produce fatigue damage; the mathematical model predicts the fatigue life of the gear by establishing the relationship between the gear parameters and the fatigue life; although it can predict the fatigue life and achieve a certain accuracy, there are still many defects. These methods are time-consuming and costly, and the results often have large deviations from the test values.

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  • Gear contact fatigue life prediction method based on GA-BP neural network
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  • Gear contact fatigue life prediction method based on GA-BP neural network

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Embodiment Construction

[0021] Below in conjunction with accompanying drawing and embodiment the present invention will be further described:

[0022] Such as figure 1 As shown, this embodiment includes the following steps:

[0023] S1. Collect the test data of the gear contact fatigue test, and use the gear parameters to analyze the influence of the gear contact fatigue life. The structural characteristic parameters and material parameters of the gear are used as input parameters, and the gear contact fatigue life is used as the output parameter. For each set of test data Perform normalization processing and use it as a training sample and a test sample.

[0024] 143 sets of gear contact fatigue life test data were collected from multiple universities and research institutes. The selected input parameters include but are not limited to the number of teeth, roughness, contact stress, tooth surface hardness and material parameters. The output parameter is the contact fatigue life of the gear. The un...

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Abstract

The invention discloses a GA-BP neural network-based gear contact fatigue life prediction method. The method comprises the following steps of 1, collecting gear contact fatigue test data and normalizing the gear contact fatigue test data as sample data of a BP neural network model; 2, constructing a structure of a BP neural network; 3, optimizing the weight and threshold of the BP neural network by using a genetic algorithm, and training the BP neural network; 4, calculating a prediction precision evaluation parameter determination coefficient, and obtaining an optimization weight and a threshold when the prediction precision evaluation parameter determination coefficient meets a set value; and 5, testing the established GA-BP neural network by using the test sample set. Compared with an existing gear contact fatigue life prediction method based on a physical model, the gear contact fatigue life prediction method is low in cost and high in prediction precision, does not need to be derived according to a failure mechanism, achieves prediction of the gear fatigue life, improves the prediction accuracy, is easy to use, and provides a new technical means for design and manufacturing ofgears.

Description

technical field [0001] The invention belongs to the technical field of intelligent manufacturing, and in particular relates to a method for predicting the contact fatigue life of gears. Background technique [0002] Gear is an important mechanical basic part, which is widely used in various industrial fields for motion and power transmission, and its fatigue life directly determines the service performance of the whole machine. With the development of major equipment such as wind power, high-speed rail, and aviation in the direction of high reliability, long life, and intelligence, higher requirements are placed on the life and reliability of gears. If the gear is fatigued and damaged during the service stage, it will not only cause equipment shutdown and affect production, but also endanger personal and property safety. Accurately predicting the fatigue life of gears can avoid sudden and vicious equipment accidents and ensure long-term safe and reliable operation of mechan...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06N3/04G06N3/08
CPCG06N3/084G06N3/086G06N3/044
Inventor 刘怀举张秀华吴少杰朱才朝魏沛堂
Owner CHONGQING UNIV
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