基于EEMD的滚动轴承故障特征主元分析和PNN建模
EEMD-Based Failure Characteristics Principal Component Analysis of Rolling Bearing and PNN Modelling
摘 要
利用滚动轴承各种工作状态下测量得到的声发射信号，建立了一种基于总体平均经验模态分解(EEMD)与概率神经网络(PNN)的滚动轴承故障特征提取和诊断方法。通过EEMD对信号进行自适应时频分解，在不同频段上分析本征模态函数(IMF)分量；计算IMF的能量值并做能量贡献分析，确定主元分量以组成故障特征向量；利用PNN网络实现故障特征向量与故障模式之间的函数映射，进行故障诊断。仿真结果和试验数据的对比证明了提出方法的有效性。
声发射
总体平均经验模态分解
概率神经网络
Rolling bearing
Acoustic emission
Ensemble empirical mode decomposition(EEMD)
Probabilistic neural network(PNN)
Abstract
Using acoustic emission signal measured under different running state of rolling bearing, a fault feature extraction and diagnosis method for rolling bearing based on the ensemble empirical mode decomposition(EEMD) and the probabilistic neural network(PNN) was built. Firstly, the EEMD method was applied to adaptively decompose the signal in time-frequency domain, and the intrinsic mode functions(IMFs) in different frequency bands were analyzed. Then the energy of IMFs was computed, and a failure feature vector was formed by the principal components selected according to the energy contribution analysis. Using PNN to approximate the functional mapping between the feature vector and the fault mode, the failure diagnosis could be realized. Comparison between simulation results and experiment data proves that the proposed method is effective.
中图分类号 TH133.33 TP206.3
所属栏目 试验研究
基金项目 国家自然科学基金资助项目(61074090)；航空基金资助项目(2011ZD54011)
收稿日期 2013/8/8
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备注席剑辉(1975-)，女，副教授，主要从事故障检测与诊断工作。
引用该论文: XI Jian-hui,HAN Yan-zhe,SU Rong-hui,FU Li. EEMD-Based Failure Characteristics Principal Component Analysis of Rolling Bearing and PNN Modelling[J]. Nondestructive Testing, 2014, 36(7): 74～78
席剑辉,韩彦哲,苏荣辉,傅莉. 基于EEMD的滚动轴承故障特征主元分析和PNN建模[J]. 无损检测, 2014, 36(7): 74～78
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参考文献
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【13】余永增，韩龙，戴光.基于声发射的滚动轴承故障诊断方法［J］.无损检测,2010,32(6):416-419, 423.
【2】Al-DOSSARY S, HAMZAH R I R, MBA D. Observations of changes in acoustic emission waveform for varying seeded defect sizes in a rolling element bearing［J］. Applied Acoustics,2009,70(1):58-81.
【3】张颖，苏宪章，刘占生. 基于周期性声发射撞击计数的滚动轴承故障诊断［J］.轴承,2011(6):38-41.
【4】程军圣，史美丽，杨宇. 基于LMD与神经网络的滚动轴承故障诊断方法［J］.振动与冲击,2010,29(8):141-144.
【5】HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis［J］. Proceedings of the Royal Society of London Series A-Mathematical Physical and Engineering Sciences,1998,454:903-995.
【6】HUANG N E, SHEN Z, LONG S R. A new view of nonlinear water waves: the Hilbert spectrum［J］. Annual Review of Fluid Mechanics,1999,31:417-457.
【7】WU Z H, HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method［J］. Advances in Adaptive Data Analysis,2009,1(1):1-41.
【8】胡爱军，马万里，唐贵基.基于集成经验模态分解和峭度准则的滚动轴承故障特征提取方法［J］. 中国电机工程学报，2012, 32(11): 106-111.
【9】SPECBT D F. Probabilistic neural network ［J］. Neural Networks,1990,3:109-118.
【10】李辉，郑海起，唐力伟.基于EEMD和THT的齿轮故障诊断方法［J］.振动、测试与诊断,2011,31(4):496-500.
【11】杜德润，仇德伦，李爱群，等.神经网络技术在土木结构健康监测中的应用［J］.无损检测,2004,26(8):383-387.
【12】陈佳，傅攀.基于小波包和概率神经网络的滚动轴承故障诊断［J］.四川兵工学报,2011,32(5):58-61.
【13】余永增，韩龙，戴光.基于声发射的滚动轴承故障诊断方法［J］.无损检测,2010,32(6):416-419, 423.
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