FAULT DIAGNOSIS OF THE PLANETARY GEARBOX BASED ON SFLA-BP MODEL AND KPCA FEATURE EXTRACTION

HE Yan; WANG ZongYan

doi:10.16579/j.issn.1001.9669.2020.02.002

您当前的位置：

首页 >

文章列表页 >

FAULT DIAGNOSIS OF THE PLANETARY GEARBOX BASED ON SFLA-BP MODEL AND KPCA FEATURE EXTRACTION

更新时间：2022-09-22

- FAULT DIAGNOSIS OF THE PLANETARY GEARBOX BASED ON SFLA-BP MODEL AND KPCA FEATURE EXTRACTION
- Journal of Mechanical Strength Vol. 42, Issue 2, Pages: 263-269(2020)
- 作者机构：
  
  1. 中北大学机械工程学院
  2. 山西省起重机数字化设计工程技术研究中心
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2020.02.002
  CLC：
扫描看全文
HE Yan, WANG ZongYan. FAULT DIAGNOSIS OF THE PLANETARY GEARBOX BASED ON SFLA-BP MODEL AND KPCA FEATURE EXTRACTION. [J]. 42(2):263-269(2020)
DOI：

HE Yan, WANG ZongYan. FAULT DIAGNOSIS OF THE PLANETARY GEARBOX BASED ON SFLA-BP MODEL AND KPCA FEATURE EXTRACTION. [J]. 42(2):263-269(2020) DOI： 10.16579/j.issn.1001.9669.2020.02.002.

摘要

混合蛙跳算法(SFLA)是将竞争进化策略和有限度随机搜索有机结合的群智能优化算法,已应用于各种优化问题。分析混合蛙跳算法用于全局优化问题的优势,并建立了基于混合蛙跳优化BP神经网络(SFLA-BP)算法模型,进行了仿真研究。以行星齿轮箱为工程实例,分析了行星齿轮故障信号传递路径复杂,相互耦合,呈现非线性的特性。应用核主元分析(KPCA)提取了时、频域敏感的特征参数,特征属性由27维压缩到9维,并建立了结构为9-14-4的神经网络故障诊断系统。充分利用混合蛙跳算法全局搜索的优势实现了行星齿轮不同磨损程度的故障诊断。诊断结果表明:SFLA-BP模型与BP神经网络相比,整体输出误差较小,诊断的准确率提高了12. 5%,对不同损伤程度的故障可以达到准确的识别效果。

Abstract

The shuffled frog leaping algorithm（ SFLA） is a swarm intelligent optimization algorithm that combines competitive evolutionary strategy and limited random search. It has been applied to various optimization problems. This paper analyzed the advantages of the SFLA for global optimization problems,and established optimization BP neural network algorithm model（ SFLA-BP） based on SFLA,and carried on the simulation study. The planetary gearbox was taken as an engineering example,and its nonlinear characteristic was presented because of transmission path of fault vibration signals complicated and their coupling with each others. The kernel principal component analysis（ KPCA） was used to extract the time domain and frequency domain sensitive features,and the feature dimensions were reduced from 27 to 9. A neural network fault diagnosis system with 9-14-4 structure was established. This paper makes full use of the advantage of the global search of SFLA to realize fault diagnosis of planetary gear with different wear levels. The diagnosis results show that the SFLA-BP model has a smaller overall output error compared with BP neural network,and the diagnostic accuracy increases by 12. 5 %,and has achieved a more accurate identification effect on different degrees damage faults.

关键词

混合蛙跳算法(SFLA)BP神经网络行星齿轮箱故障诊断核主元分析(KPCA)

Keywords

Shuffled frog leaping algorithm（SELA）BP neural networkPlanetary gearboxFault diagnosisKernel principal component analysis（KPCA）

references

Views

256

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

MULTI-OBJECTIVE ROBUST OPTIMIZATION DESIGN OF COMPLIANT HINGE BASED ON BP NEURAL NETWORK (MT)

APPLICATION OF SEMI SUPERVISED LAPLACE SCORE IN ROLLING BEARING FAULT DIAGNOSIS (MT)

A SINGULAR INTERVAL RECONSTRUCTION LOCAL MEAN DECOMPOSITION AND ITS APPLICATION IN BEARING FAULT DIAGNOSIS OF RECIPROCATING COMPRESSOR (MT)

FAULT DIAGNOSIS OF GEARBOX BASED ON ADAPTIVE EEMD AND FAST KURTOGRAM

RESEARCH ABOUT FAULT DIAGNOSIS OF BEARING BASED ON INSTRINSIC TIME SCALE DECOMPOSITION AND CONVOLUTIONAL NEURAL NETWORK

Related Author

No data

Related Institution

School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology

School of Mechanical Engineering, Shenyang University of Technology

BMW Brilliance Automotive Co., Ltd.

School of Mechanical Science and Engineering, Northeast Petroleum University

Shandong Fenghui Equipment Technology Co., Ltd.

⁰