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Method for predicting vibration by gear microscopic errors based on neural network

A prediction method and neural network technology, applied in the field of gearboxes, can solve problems such as not using vibration prediction methods

Active Publication Date: 2021-07-02
CHONGQING TSINGSHAN IND
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] The purpose of the present invention is to provide a neural network-based method for predicting vibration of gear microscopic errors, aiming at solving the problem of not using the method for predicting vibration of gear microscopic errors in the prior art

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  • Method for predicting vibration by gear microscopic errors based on neural network
  • Method for predicting vibration by gear microscopic errors based on neural network
  • Method for predicting vibration by gear microscopic errors based on neural network

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Embodiment Construction

[0034] Embodiments of the present invention are described in detail below, see figure 1 with figure 2 , the embodiment of the present invention provides a neural network-based method for predicting vibration of gear microscopic errors, comprising the following steps:

[0035] Gear micro-errors of metering gearboxes;

[0036] Calculate the comprehensive error of the intermeshing gear pair of the gearbox according to the microscopic error of the gear;

[0037] Normalize each comprehensive error;

[0038] Test the vibration value of the gearbox;

[0039] Using the calculated comprehensive error of the intermeshing gear pair and the tested vibration value as training samples to train the established neural network model;

[0040] After the training of the neural network model is completed, calculate the comprehensive error of the intermeshing gear pair of the gearbox to be tested, input the newly calculated comprehensive error of the intermeshing gear pair of the gearbox into...

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Abstract

The invention discloses a method for predicting vibration by gear microscopic errors based on a neural network. The method comprises the steps of metering the gear microscopic errors, calculating the comprehensive errors of mutually engaged gear pairs, carrying out the normalization processing of each comprehensive error, testing a vibration value through an offline detection platform, carrying out normalization processing on the vibration values of different detection platforms, establishing a neural network model by using the comprehensive errors and the vibration values, inputting the comprehensive errors into the model to output a predicted vibration value, and when the trained neural network tends to be stable, inputting a new gear pair comprehensive error into the neural network model, and outputting the predicted vibration value. According to the method for predicting the vibration by the gear microcosmic errors based on the neural network, the vibration value of a gearbox can be judged through all the comprehensive errors of the gear pair, and then whether the NVH performance of the gearbox meets the requirement or not is judged.

Description

technical field [0001] The invention relates to the technical field of gearboxes, in particular to a neural network-based method for predicting vibration of gear microscopic errors. Background technique [0002] The vibration noise of automobile gearbox is generated by dynamic excitation (gear transmission error), and the comprehensive error of gear pair is caused by gear machining and installation error, which is one of the main dynamic excitations in the gear meshing process. When the gearbox is off-line, it is necessary to test and monitor the vibration caused by the meshing of the gearbox gears. The existing technology uses an off-line test bench to test the vibration value Y of the gearbox, and the test efficiency is relatively slow. There is no such method in the prior art. The prediction method of gearbox vibration does not use the prediction method of gear microscopic error on vibration. Contents of the invention [0003] The purpose of the present invention is to...

Claims

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Application Information

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IPC IPC(8): G01M13/021G01M13/028G06N3/08G06N3/04
CPCG01M13/021G01M13/028G06N3/084G06N3/048Y02T90/00
Inventor 刘子谦彭天河冯楠孙宇范莎
Owner CHONGQING TSINGSHAN IND
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