Study on Fault Diagnosis Method of Bearing based on Shuffled Frog Leaping Algorithm to Optimize the BP Neural Network

Wang Yu; Wei Xiuye

doi:10.16578/j.issn.1004.2539.2017.05.026

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Study on Fault Diagnosis Method of Bearing based on Shuffled Frog Leaping Algorithm to Optimize the BP Neural Network

更新时间：2022-10-20

- Study on Fault Diagnosis Method of Bearing based on Shuffled Frog Leaping Algorithm to Optimize the BP Neural Network
- Vol. 41, Issue 5, Pages: 127-131(2017)
- 作者机构：
  
  1. 中北大学机械与动力工程学院
  2. 临汾职业技术学院
  3. 先进制造技术山西省重点实验室
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2017.05.026
  CLC：
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[1]王宇,魏秀业.基于混合蛙跳优化神经网络的轴承故障诊断研究[J].机械传动,2017,41(05):127-131.

Wang Yu, Wei Xiuye. Study on Fault Diagnosis Method of Bearing based on Shuffled Frog Leaping Algorithm to Optimize the BP Neural Network. [J]. 41(5):127-131(2017)
[1]王宇,魏秀业.基于混合蛙跳优化神经网络的轴承故障诊断研究[J].机械传动,2017,41(05):127-131. DOI： 10.16578/j.issn.1004.2539.2017.05.026.

Wang Yu, Wei Xiuye. Study on Fault Diagnosis Method of Bearing based on Shuffled Frog Leaping Algorithm to Optimize the BP Neural Network. [J]. 41(5):127-131(2017) DOI： 10.16578/j.issn.1004.2539.2017.05.026.

摘要

以滚动轴承故障诊断为课题研究背景,以JZQ250型号传动箱为实验对象,将混合蛙跳算法与BP神经网络进行结合,利用混合蛙跳算法高效的计算性能和优良的全局搜索能力,对BP神经网络的网络结构进行优化。通过对比发现,混合蛙跳算法优化后的BP神经网络模型,可避免BP神经网络在训练中陷入局部最优,减短训练时间,提高训练准确度,具有较高收敛速度和准确诊断能力。通过一系列训练和测试,结果表明,这种方法能够提高诊断的可靠性和准确性。

Abstract

Based on the background of rolling bearing fault diagnosis,taking the JZQ250 type transfer-box as test object,the shuffled frog leaping algorithm（ SFLA） is combined with back propagation（ BP） neural network,by using the efficient computing performance and the excellent ability of global optimization of shuffled frog leaping algorithm,the network structure of BP neural network is optimized. Through comparison,it is found that the BP neural network model optimized by shuffled frog leaping algorithm can avoid making it fall into local optimum,reduce the training time and improve the training accuracy during the training of the network,and have several advantages,such as relatively higher convergence rate and ability to accurately diagnose.Through a series of training and testing,the results show that this approach can improve the accuracy and reliability of fault diagnosis.

关键词

滚动轴承混合蛙跳算法BP神经网络故障诊断

Keywords

Rolling bearingShuffled frog leaping algorithmBP neural networkFault diagnosis

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