摘要
针对行星齿轮箱振动信号故障特征提取困难的问题,提出了一种基于EMD-SVD与概率神经网络相结合的故障诊断方法。首先,利用经验模态分解方法将去噪后的振动信号自适应地分解为多个本征模函数。其次,利用相关系数和方差贡献率选取一定量的本征模函数,并将其构成的矩阵进行奇异值分解得到特征向量。最后,将特征向量输入概率神经网络进行故障诊断。在行星齿轮箱故障诊断实验台上进行了实验,并与基于能量熵构成的特征向量进行了对比,结果验证了该方法的有效性。
In order to solve the difficulty in fault feature extraction of the planetary gearbox vibration signal,a comprehensive fault diagnosis method based on empirical mode decomposition(EMD),singularity value decomposition(SVD)and probabilistic neural networks(PNN)is proposed.Firstly,with the EMD method,de-noised vibration signals are decomposed into a finite number of intrinsic mode function(IMF).Secondly,some IMF components are selected according to the criterion of correlation coefficient and variance contribution ratio,and singular value sequences regarded as eigenvectors are obtained with the method of SVD.Lastly,the eigenvectors serve as import of PNN so that faults of the planetary gearbox are recognized.Experiments are conducted on the planetary gearbox fault diagnosis test-bed and comparison is carried out with eigenvectors made of energy entropy,which fully validates the effectiveness of the proposed methodology.
作者
张安安
黄晋英
卫洁洁
庞宇
Zhang An′an;Huang Jinying;Wei Jiejie;Pang Yu(School of Mechanical Engineering, North University of China, Taiyuan 030051, China;School of Data Science and Technology, North University of China, Taiyuan 030051, China)
出处
《机械传动》
CSCD
北大核心
2018年第12期160-165,共6页
Journal of Mechanical Transmission
关键词
行星齿轮箱
经验模态分解
奇异值分解
概率神经网络
故障诊断
Planetary gearbox
Empirical mode decomposition
Singularity value decomposition
Probablistic neural networks
Fault diagnosis
作者简介
张安安(1991— ),男,山东临沂人,硕士研究生,研究方向为信号处理与故障诊断。
