上海交通大学学报(自然版) ›› 2016, Vol. 50 ›› Issue (05): 723-729.
李庆1,LIANG Steven Y1,2,杨建国1
收稿日期:
2015-07-01
出版日期:
2016-05-28
发布日期:
2016-05-28
基金资助:
LI Qing1,LIANG Steven Y1,2,YANG Jianguo1
Received:
2015-07-01
Online:
2016-05-28
Published:
2016-05-28
摘要: 摘要: 根据谐波小波分解非平稳振动信号优良特性与隐马尔科夫(HMM)模型的时序模式分类能力,提出了一种基于谐波小波样本熵与HMM模型结合的轴承故障模式识别方法.该方法首先利用谐波小波对轴承各个状态故障信号进行分解,进而由谐波小波三维时频网格图的频率层数特征计算合理的样本熵维数和阈值,依次提取轴承振动信号各层的样本熵构成特征向量序列;然后将序列前120组输入HMM模型中进行训练得到对应故障模型,剩余80组进行测试与识别,通过对比对数似然估计概率输出值确定轴承故障类型.实验通过与BP和RBF神经网络模型进行不同训练组数的正确识别率对比,验证了该组合方法具有识别准确率高,稳定性强的优点.
中图分类号:
李庆1,LIANG Steven Y1,2,杨建国1. 谐波小波样本熵与HMM模型的轴承故障模式识别[J]. 上海交通大学学报(自然版), 2016, 50(05): 723-729.
LI Qing1,LIANG Steven Y1,2,YANG Jianguo1. Bearing Fault Pattern Recognition Using Harmonic Wavelet Sample Entropy and Hidden Markov Model[J]. Journal of Shanghai Jiaotong University, 2016, 50(05): 723-729.
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