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Gear residual life prediction method based on MMALSTM

A prediction method and gear technology, applied in the fields of instrumentation, computing, electrical and digital data processing, etc., can solve the problems of wasting computing resources, limited long-term information storage capacity, affecting the speed and accuracy of neural network model training, etc.

Active Publication Date: 2019-08-27
CHONGQING UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

First of all, the long-term information storage capacity of LSTM is also limited. The storage of redundant information is not conducive to life prediction and will waste computing resources in vain.
And the existence of irrelevant and / or redundant features will affect the speed and accuracy of neural network model training

Method used

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  • Gear residual life prediction method based on MMALSTM
  • Gear residual life prediction method based on MMALSTM
  • Gear residual life prediction method based on MMALSTM

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0079] According to the LTSMPP neural network model and prediction method proposed above, the experiment will be carried out below. In this experiment, the first-stage transmission is accelerated and the second-stage transmission is decelerated, so that the transmission ratio of the experimental gearbox is 1:1. The amount of lubricating oil in the experimental gearbox is 4L / h, and the cooling temperature is 70 degrees. Among them, the gear running platform is used; the torsion controller is used to control the torque applied to the gear in the test; the cooling and lubrication controller is used to control the cooling and lubrication of the gear in the experiment; the actual operation platform is used to operate the whole experiment switch and set some experimental parameters. The material used for the experimental gear is 40Cr, the machining accuracy is grade 5, the surface hardness is 55HRC, and the modulus is 5. In particular, the number of teeth of the large gear is 31, ...

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Abstract

The invention relates to a gear residual life prediction method based on MMALSTM, and belongs to the field of big data and intelligent manufacturing. The method comprises the following steps: firstly,simplifying and fusing high-dimensional features of acquired gear vibration signals; then using the fusion feature information subjected to dimension reduction for multi-step prediction of MMALSTM, and performing macro and micro processing on the fusion feature data by adopting MMA according to the characteristic that different feature information contains different information amounts; and finally, amplifying the weights of the input data and the recursive data according to the result of the MMA, and performing automatic and different degrees of processing on the fusion feature data. The prediction speed and precision of the residual life of the gear can be improved while the calculated amount is reduced.

Description

technical field [0001] The invention belongs to the field of big data and intelligent manufacturing, and relates to a method for predicting the remaining life of a gear based on MMALSTM. Background technique [0002] Gears are widely used in mechanical equipment and are one of the most widely used mechanical parts. Gears have unique advantages such as high transmission efficiency, compact structure, good transmission smoothness, large carrying capacity, and long service life, which make them have strong and lasting vitality. Under complex working conditions and environments, gears are prone to failure, which may lead to disasters in machine operation and even endanger personal safety. This is especially true for large or very large equipment, such as hydroelectric generators, mining conveying machinery, helicopter power transmission systems, heavy machine tools, etc. The life prediction of in-service gears can effectively determine the maintenance time of equipment, improv...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/17G06F2119/04Y02T90/00
Inventor 秦毅项盛金磊王阳阳
Owner CHONGQING UNIV
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