Method for predicting residual life of gear based on LSTM-AON

A prediction method and technology for gears, which are applied in neural learning methods, biological neural network models, design optimization/simulation, etc., can solve the problems of insufficient data analysis of gear degradation, poor life prediction ability, and use of neural network sequential information mining, etc. question

Pending Publication Date: 2020-07-31
CHONGQING UNIV
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  • Abstract
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  • Application Information

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Problems solved by technology

However, the traditional LTSM network does not analyze the gear degradation data comprehensively enough,

Method used

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  • Method for predicting residual life of gear based on LSTM-AON
  • Method for predicting residual life of gear based on LSTM-AON
  • Method for predicting residual life of gear based on LSTM-AON

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Embodiment Construction

[0061] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments are only schematically illustrating the basic concept of the present invention, and the following embodiments and the features in the embodiments can be combined with each other in the case of no conflict.

[0062] see Figure 1 to Figure 8 , Figure 5 It is a flowchart of a method for predicting the remaining life of gears based on LSTM-AON, which specifically ...

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Abstract

The invention relates to a gear residual life prediction method based on LSTM-AON, and belongs to the field of big data and intelligent manufacturing. According to the invention, the method comprisesthe steps: designing a novel LSTM-AON neural network to predict the residual life of the gear; based on an LSTM network, improving a hidden layer of the LSTM-AON network, so the LSTM-AON network selects an interlayer position where an element with the maximum attention coefficient is located as a hierarchical position by calculating the attention coefficients of related elements and labels; guiding the division of the hierarchical structure by attention and is determined by an element most similar to the label; and embedding the tree-shaped hierarchical structure into the recurrent neural network through the attention degree of the information so as to mine the inter-layer sequence information ignored by the traditional network. Compared with a traditional LSTMs network, the LSTM-AON network adopted by the method is high in calculation speed, small in calculation amount and higher in precision.

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 gears based on LSTM-AON. 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...

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Application Information

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IPC IPC(8): G06F30/27G06N3/04G06N3/08G06F119/04
CPCG06N3/08G06N3/048G06N3/044G06N3/045
Inventor 秦毅项盛阎昊冉朱才朝
Owner CHONGQING UNIV
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