Application of GA-ACO Optimized BP Neural Network in Fault Diagnosis of Planetary Gearbox

Application of GA-ACO Optimized BP Neural Network in Fault Diagnosis of Planetary Gearbox

Aiming at the problems of low fault recognition rate， slow convergence speed and difficult parameter selection in the process of fault diagnosis of planetary gearbox based on BP neural network improved by optimization algorithm， a GA-ACO algorithm is proposed to optimize the parameters of neural net...

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Bibliographic Details
Main Authors: Chang Gao, Zhongqing Yu, Qiang Zhou
Format: Article
Language:zho
Published: Editorial Office of Journal of Mechanical Transmission 2021-03-01
Series:Jixie chuandong
Subjects:
GA-ACO-BP algorithm
Planetary gearbox
Fault diagnosis
Genetic algorithm
Ant colony optimization algorithm
BP neural network
Online Access:http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.03.025
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Summary:Aiming at the problems of low fault recognition rate， slow convergence speed and difficult parameter selection in the process of fault diagnosis of planetary gearbox based on BP neural network improved by optimization algorithm， a GA-ACO algorithm is proposed to optimize the parameters of neural network. The basic principle and main steps of GA-ACO-BP algorithm are given. At the same time， this method is applied to the fault diagnosis of planetary gearbox. Comparing the performance of ACO-BP neural network algorithm and GA-ACO-BP algorithm， the results show that the convergence speed of ACO Optimized BP neural network is slow and the recognition accuracy is not high， while GA-ACO-BP algorithm can accurately and quickly diagnose and identify the fault of planetary gearbox.
ISSN:1004-2539

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