[1]朱静,邓艾东,邓敏强,等.基于RSIFICA的行星齿轮箱故障诊断方法[J].东南大学学报(自然科学版),2020,50(2):377-384.[doi:10.3969/j.issn.1001-0505.2020.02.023]
 Zhu Jing,Deng Aidong,Deng Minqiang,et al.Fault diagnosis method of planetary gear box based on RSIFICA[J].Journal of Southeast University (Natural Science Edition),2020,50(2):377-384.[doi:10.3969/j.issn.1001-0505.2020.02.023]
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基于RSIFICA的行星齿轮箱故障诊断方法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
50
期数:
2020年第2期
页码:
377-384
栏目:
能源与动力工程
出版日期:
2020-03-20

文章信息/Info

Title:
Fault diagnosis method of planetary gear box based on RSIFICA
作者:
朱静1邓艾东1邓敏强1翟怡萌1孙文卿1王姗2
1 东南大学火电机组振动国家工程研究中心, 南京210096; 2中国能源建设集团安徽省电力设计院, 合肥230601
Author(s):
Zhu Jing1 Deng Aidong1 Deng Minqiang1 Zhao Yimeng1 Sun Wenqing1 Wang Shan2
1National Engineering Research Center of Turbo-Generator Vibration, Southeast University, Nanjing 210096, China
2Anhui Electric Power Design Institute, China Energy Engineering Group, Hefei 230601, China
关键词:
行星齿轮箱 共振稀疏分解 快速独立分量分析 故障诊断
Keywords:
planetary gearbox resonance sparse signal decomposition(RSSD) fast independent component analysis(FastICA) fault diagnosis
分类号:
TK83
DOI:
10.3969/j.issn.1001-0505.2020.02.023
摘要:
为了对行星齿轮箱进行故障检测和诊断,提出了一种基于共振稀疏快速独立分量的分析方法(RSIFICA).该方法首先采用共振稀疏分解对信号进行降维预处理,进行二次共振稀疏分解,构造虚拟通道增加传感器通道数目,同时引入牛顿-辛普森公式对快速独立分量分析方法进行改进.该方法减少包含瞬态冲击的宽带信号的影响,实现信号中振源信号数目的降维.同时,二次分解增加输入FastICA的通道数,解决了独立分量分析方法在提取行星齿轮箱故障特征频率过程中出现欠定盲源和收敛速度缓慢问题,同时提高了FastICA的运算效率.将该方法应用到行星齿轮箱的故障诊断中,包络谱分析结果表明,RSIFICA能准确地提取行星齿轮箱断齿故障特征频率,有效地解决了FastICA的问题,计算效率提高了21.49%.对比实验表明,相比于EMD-FastICA联合方法,RSIFICA能够对齿轮微弱故障特征进行更为有效的提取.
Abstract:
To detect and diagnose the planetary gearbox, a method based on resonance sparse improved fast independent component analysis(RSIFICA)was proposed. Firstly, the resonance sparse signal decomposition(RSSD)was used to reduce the dimensionality of the signal and the Newton-Simpson formula was introduced to improve the fast independent component analysis(FastICA). The method reduces the influence of wideband signals including transient impacts and the number of vibration source signals in the signal. Meanwhile, the secondary decomposition increased the number of input channels of FastICA, and solved the problems of underdetermined blind sources and slow convergence speeds in the process of extracting the characteristic frequency of the planetary gearbox by the independent component analysis method. Thus, the computing efficiency of FastICA was also improved. The method was applied to the fault diagnosis of planetary gearboxes. The analysis results of envelope spectrum show that RSIFICA can accurately extract the characteristic frequency of broken gear faults of planetary gearboxes, and the calculation efficiency of FastICA is improved by 21.49%. Comparative experiments show that RSIFICA can solve FastICA problems for accurately diagnosing planetary gearbox faults.

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相似文献/References:

[1]朱静,邓艾东,邓敏强,等.基于MED和自适应VMD的行星齿轮箱故障诊断方法[J].东南大学学报(自然科学版),2020,50(4):698.[doi:10.3969/j.issn.1001-0505.2020.04.014]
 Zhu Jing,Deng Aidong,Deng Minqiang,et al.Fault diagnosis of planetary gearbox based on minimum entropy deconvolution and adaptive variational mode decomposition[J].Journal of Southeast University (Natural Science Edition),2020,50(2):698.[doi:10.3969/j.issn.1001-0505.2020.04.014]

备注/Memo

备注/Memo:
收稿日期: 2019-09-05.
作者简介: 朱静(1993—),女,博士生;邓艾东(联系人),男,博士,教授,博士生导师,dnh@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(51875100).
引用本文: 朱静,邓艾东,邓敏强,等.基于RSIFICA的行星齿轮箱故障诊断方法[J].东南大学学报(自然科学版),2020,50(2):377-384. DOI:10.3969/j.issn.1001-0505.2020.02.023.
更新日期/Last Update: 2020-03-20