齿轮箱故障非线性特征测度及状态TWSVM辨识研究

曾柯,柏林

振动与冲击 ›› 2018, Vol. 37 ›› Issue (15) : 179-184.

PDF(1053 KB)
PDF(1053 KB)
振动与冲击 ›› 2018, Vol. 37 ›› Issue (15) : 179-184.
论文

齿轮箱故障非线性特征测度及状态TWSVM辨识研究

  • 曾柯,柏林
作者信息 +

Nonlinear characteristic measure of gearbox faults and their category identification with TWSVM

  • ZENG Ke,BO Lin
Author information +
文章历史 +

摘要

针对齿轮箱振动的非线性性,利用非线性特征测度的方法提取齿轮箱振动信号的故障特征。并利用双子支持向量机(Twin Support Vector Machine,简称TWSVM)对齿轮箱故障类别的辨识性能进行研究。TWSVM努力构造两个非平行的超平面来实现分类,它比支持向量机(Support Vector Machine,简称SVM)针对多分类问题具有更好的样本不均衡适应性,并且分类性能优势明显。对齿轮箱故障类别辨识的实验表明,与传统的SVM和BP神经网络算法相比较,TWSVM具有更高的分类准确率。

Abstract

Aiming at gearbox vibration’s nonlinearity, its vibration signals’ fault features were extracted with the method of nonlinear characteristic measure. The twin support vector machine (TWSVM) technique was used to study gearbox’s fault category identification. With TWSVM, two nonparallel hyperplanes were constructed to realize classification, and it had a better adaptability than the support vector machine (SVM) technique did when samples were imbalance, and its classification performance had obvious advantages. Simulation tests for gearbox fault category identification showed that TWSVM has a higher classification accuracy rate than the BP neural network method and SVM do.


关键词

齿轮箱 / 故障诊断 / 非线性特征 / TWSVM

Key words

 gearbox / fault diagnosis / nonlinear characteristic / TWSVM

引用本文

导出引用
曾柯,柏林. 齿轮箱故障非线性特征测度及状态TWSVM辨识研究[J]. 振动与冲击, 2018, 37(15): 179-184
ZENG Ke,BO Lin. Nonlinear characteristic measure of gearbox faults and their category identification with TWSVM[J]. Journal of Vibration and Shock, 2018, 37(15): 179-184

参考文献

[1]庞茂. 汽车主减速器振动信号非线性特征研究 [D]; 浙江大学, 2006.
[2]刘永斌. 基于非线性信号分析的滚动轴承状态监测诊断研究 [D]; 中国科学技术大学, 2011.
[3]董洪波, 申中杰, 姚亚峰. 基于TWSVM的煤矿井下钻机轴承故障诊断 [J]. 煤矿机械, 2015, 36(5): 298-300.
DONG Hongbo,SHEN Zhongjie, YAO Yafeng. Based on TWSVM drill bearing in coal mine fautt diagnosis [J]. Coal Mine Machinery, 2015, 36(5):298-300.
[4]王震. 基于双重支持向量机的分类算法研究 [D]; 吉林大学, 2010.
[5]易辉, 宋晓峰, 姜斌, et al. 样本不均衡条件下基于自调整支持向量机的故障诊断 [J]. 北京理工大学学报, 2013, 33(4): 394-398.
YI Hui, SONG Xiaofeng, JIANG Bin, et al. Fault diagnosis based on self-tuning support vector machine in sample unbalance condition [J]. Transactions of Beijing Institute of Technology, 2013, 33(4):394-398.
[6]JAYADEVA, KHEMCHANDANI R, CHANDRA S. Twin Support Vector Machines for pattern classification [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2007, 29(5): 905-10.
[7]谢娟英, 张兵权, 汪万紫. 基于双支持向量机的偏二叉树多类分类算法 [J]. 南京大学学报:自然科学版, 2011, 47(4): 354-63.
XIE Juanying, ZHANG Bingquan, WANG Wanzi. A partial binary tree algorithm for multiclass classification based on twin support vector machines [J]. Journal of Nanjing University(Natural Sciences), 2011, 47(4): 354-63.
[8]刘建明. 基于粒子群算法的双子支持向量机研究 [J]. 软件导刊, 2015, 06): 72-5.
LIU Jianming. Research on twin Support vector machines based on particle swarm optimization [J]. Software Guide, 2015, 14(6): 72-75.
[9]于俊钊. 孪生支持向量机及其优化方法研究 [D]; 中国矿业大学, 2014.
[10]BISHOP C M. Neural Networks for Pattern Recognition [J]. Agricultural Engineering International the Cigr Journal of Scientific Research & Development Manuscript Pm, 1995, 12(5): 1235 - 42.
[11]郝研. 分形维数特性分析及故障诊断分形方法研究 [D]; 天津大学, 2012.
[12]胥永刚, 何正嘉. 分形维数和近似熵用于度量信号复杂性的比较研究 [J]. 振动与冲击, 2003, 22(3): 25-7.
XU Yonggang, HE Zhengjia. Research on comparison between approximate entropy and fractal dimension for complexity measure of signals [J]. Journal of Vibration and Shock, 2003, 22(3): 25-7.
[13]PINCUS S M. Approximate entropy as a measure of system complexity [J]. Proceedings of the National Academy of Sciences, 1991, 88(6): 2297-301.
[14]赵志宏, 杨绍普. 一种基于样本熵的轴承故障诊断方法 [J]. 振动与冲击, 2012, 31(6): 136-40.
ZHAO Zhihong, YANG Shaopu. Sample entropy-based roller bearing fault diagnosis method [J]. Journal of Vibration and Shock, 2012, 31(6):136-140.
[15]刘慧, 谢洪波, 和卫星, et al. 基于模糊熵的脑电睡眠分期特征提取与分类 [J]. 数据采集与处理, 2010, 04): 484-9.
LIU Hui, XIE Hongbo, HE Weixing, et al. Characterization and classification of EEG sleep stage based on fuzzy entropy [J]. Journal of Data Acquisition & Processing, 2010, 25(4):484-9.
[16]徐可君, 夏毅锐, 江龙平. 基于Kolmogorov熵的转子-机匣系统故障诊断研究 [J]. 海军航空工程学院学报, 2006, 21(4): 437-40.
XU Kejun, XIA Yirui, JIANG Longping. Fault diagnosis research of rotor-case system based on Kolmogorov entropy [J]. Journal of naval aeronautical engineering institute, 2006, 21(4):437-440.
[17]黄明英, 王德明, 朱志宇. Kolmogorov熵在船舶电力系统可靠性研究中的应用 [J]. 舰船科学技术, 2009, 31(3): 60-3.
HUANG Mingying, WANG Deming, ZHU Zhiyu. Application of Kolmogorov entropy in reliability research of ship power system [J]. Ship Science And Technology, 2009, 31(3):60-63.
[18]刘子军. 基于TSVM的铁路电力系统谐波检测方法研究 [D]; 重庆大学, 2015.
 

PDF(1053 KB)

Accesses

Citation

Detail

段落导航
相关文章

/