摘要
针对行星齿轮箱故障诊断中存在的故障诊断样本数少、故障诊断精度低等问题,提出一种经验模态分解(EMD)、峭度排序和BP神经网络相结合的故障诊断方法。该方法首先对原始振动信号进行EMD,然后对分解获得的固有模态函数(IMF)进行峭度排序;根据训练样本数自适应地选择对应的IMF,将对应IMF的能量值作为特征向量输入BP神经网络进行故障模型的搭建,并完成模型诊断成功率测试。结果表明:经过峭度排序后的故障特征的区分度明显升高,表明改进后的算法可以在训练样本少和训练数据不均衡的情况下达到较高的故障诊断成功率,完成对行星齿轮箱故障模式的识别。
Aiming at the problems like short of samples and low accuracy in the fault diagnosis of planetary gearboxes,a fault diagnosis method that combined with empirical mode decomposition(EMD),kurtosis-ranking and BP neural network was proposed.By using this method,the original vibration signal was decomposed by EMD,then the kurtosis-ranking for the inherent mode function(IMF)obtained by decomposition was carried out;the corresponding IMF was adaptively selected according to the number of training samples,and the energy value of selected IMF was input as feature vector into the BP neural network to build the fault model and complete the model diagnosis success rate test.The results show that the distinguishing degree of fault features is significantly increased after kurtosis-ranking,indicating that by using the improved algorithm,higher diagnosis success rate can be achieved when there are few training samples or uneven training data,and the indentification of failture mode of the planetary gearboxes can be completed.
作者
刘祥雄
乐威
王帅星
雷东
LIU Xiangxiong;LE Wei;WANG Shuaixing;LEI Dong(CHN Energy Yuannan New Energy Co.,Ltd.,Kunming Yunnan 650200,China;School of Power and Mechanical Engineering,Wuhan University,Wuhan Hubei 430072,China)
出处
《机床与液压》
北大核心
2022年第3期187-192,共6页
Machine Tool & Hydraulics
基金
国家电网校企合作创新项目(GDYN-2019-FW-0389-XNY-0026)。
关键词
行星齿轮箱
故障诊断
经验模态分解
峭度排序
特征构造
Planetary gearboxes
Fault diagnosis
Empirical mode decomposition
Kurtosis-ranking
Feature construction
