Gear residual life prediction method based on cocktail long-short-term memory neural network

A long-short-term memory and neural network technology, applied in the field of prediction of the remaining life of gears, can solve problems such as economic losses, safety crises, casualties, and chain failure reactions

Pending Publication Date: 2020-09-29
CHONGQING UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

Gear faults, such as pitting, spalling and other fatigue damage, often lead to a chain failure reaction of the entire machine equipment, causing machine shutdown, and even casualties in severe cases, causing huge economic losses and safety crises

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  • Gear residual life prediction method based on cocktail long-short-term memory neural network
  • Gear residual life prediction method based on cocktail long-short-term memory neural network
  • Gear residual life prediction method based on cocktail long-short-term memory neural network

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Embodiment Construction

[0070] 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.

[0071] Wherein, the accompanying drawings are for illustrative purposes only, and represent only schematic diagrams, rather than physical drawings, and should...

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Abstract

The invention relates to a gear residual life prediction method based on a cocktail long short-term memory neural network, and belongs to the technical field of automation. The method comprises the following steps: S1, constructing gear health index based on variational self-encoding; S2, defining a cocktail long and short term memory network C-LSTM; and S3, predicting the residual life of the gear based on the health index constructed by the VAE and the C-LSTM. According to the method, firstly, health indexes capable of accurately showing the gear health state degradation trend are formed onthe basis of a variational auto-encoder (VAE), then unknown health indexes are predicted step by step according to a proposed cocktail long-short-term neural network, and predicted RUL can be obtainedwhen a set threshold value is reached.

Description

technical field [0001] The invention belongs to the technical field of automation and relates to a prediction method for the remaining life of a gear based on a cocktail long-short-term memory neural network. Background technique [0002] As a key component, gears are widely used in industrial fields, such as wind turbines, automobiles, aircraft engines, etc. Gear failures, such as pitting, spalling and other fatigue damage, often lead to a chain failure reaction of the entire machine equipment, causing machine shutdown, and even casualties in severe cases, causing huge economic losses and safety crises. A gear's remaining useful life (RUL), defined as the length of time from the present moment to the end of its useful life, is a viable strategy for determining equipment maintenance schedules and avoiding unexpected gear failures. The life prediction of in-service gears can effectively determine the maintenance time of equipment, improve production efficiency, ensure contin...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06F30/17G01M13/028G01M13/021G06N3/04G06N3/08G06F119/04
CPCG06F30/27G06F30/17G01M13/021G01M13/028G06N3/049G06N3/08G06F2119/04G06N3/045
Inventor 秦毅项盛陈定粮
Owner CHONGQING UNIV
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