Please wait a minute...
J4  2010, Vol. 44 Issue (8): 1519-1524    DOI: 10.3785/j.issn.1008-973X.2010.08.015
机械工程     
基于局域波法和KPCA-LSSVM的滚动轴承故障诊断
杨先勇1,2,周晓军1,张文斌1,杨富春1
1.浙江大学 浙江省先进制造技术重点实验室,浙江 杭州 310027;2.中国舰船研究设计中心,湖北 武汉 430064
Rolling bearing fault diagnosis based on local wave method
and KPCA-LSSVM
YANG Xian-yong1,2, ZHOU Xiao-jun1, ZHANG Wen-bin1, YANG Fu-chun1
1. Zhejiang Provincial Key Laboratory of Advanced Manufacturing Technology, Zhejiang University,
Hangzhou 310027, China; 2. China Ship Development and Design Center, Wuhan 430064, China
 全文: PDF  HTML
摘要:

针对故障滚动轴承振动信号具有非平稳性,提出基于局域波法和核主元分析最小二乘支持向量机(KPCA LSSVM )的故障诊断方法.先对轴承振动信号进行局域波分解得到若干内禀模式函数(IMF),分别计算各IMF分量的特征能量、奇异值和AR模型参数作为原始特征向量,再用KPCA将原始特征向量映射到高维特征空间提取主元构造新的特征向量,将其作为LSSVM分类器的输入来实现轴承的故障诊断.故障诊断试验结果表明,KPCALSSVM诊断方法通过KPCA得到更多的识别信息,改善了LSSVM的分类性能,相对于直接LSSVM诊断方法具有更优的泛化性,可准确识别轴承的故障类别和严重程度.

Abstract:

Aimed at the nonstationary characteristics of rolling bearing vibration signal, a fault diagnosis method was proposed based on localwave method and KPCA(kernel principal component analysis)  LSSVM(least squares support vector machine). Firstly, local wave decomposition was used to decompose rolling bearing vibration signal into several intrinsic mode function (IMF), whose feature energy, singular values and AR model parameters were computed as initial feature vectors. Secondly, ini tial feature vectors were mapped into a higherdimensional space with KPCA, and the kemel principal components were extracted as new feature vectors, which used as the input of LSSVM for fault classification. The experimental results show the KPCALSSVM method improves LSSVMs classification performance by KPCA obtaining additional discriminative information, and has better generalization than direct LSSVM method, and can identify rolling bearing fault patterns more accurately.

出版日期: 2010-09-21
:     
作者简介: 杨先勇(1980-),男,湖北孝感人,博士生,从事车辆检测、信号分析、设备故障诊断研究. E-mail:ycwwyt@yahoo.com.cn
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  

引用本文:

杨先勇, 周晓军, 张文斌, 杨富春. 基于局域波法和KPCA-LSSVM的滚动轴承故障诊断[J]. J4, 2010, 44(8): 1519-1524.

YANG Xian-Yong, ZHOU Xiao-Jun, ZHANG Wen-Bin, YANG Fu-Chun. Rolling bearing fault diagnosis based on local wave method
and KPCA-LSSVM. J4, 2010, 44(8): 1519-1524.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2010.08.015        http://www.zjujournals.com/eng/CN/Y2010/V44/I8/1519

[1] SUYKENS J A K,VANDEWALLE J.Least squares support vectors machine classifiers  [J].Neural Processing Letters, 1999, 9(3): 293300.
[2] 甘良志, 孙宗海, 孙优贤. 稀疏最小二乘支持向量机[J]. 浙江大学学报:工学版, 2007, 41(2): 245248.
GAN Liangzhi, SUN Zonghai, SUN Youxian. Sparse least squares support vector machine  [J]. Journal of Zhejiang University:Engineering Science, 2007,41(2): 245248.
[3] OJEDA F, SUYKENS J A K, MOOR B D. Low rank updated LSSVM classifiers for fast variable selection [J]. Neural Networks, 2008, 21(2/3): 437449.
[4] 康海英, 栾军英, 郑海起, 等. 基于阶次跟踪和经验模态分解的滚动轴承包络解调分析[J]. 机械工程学报, 2007, 43(8): 119122.
KANG Haiying, LUAN Junying, ZHENG Haiqi, et al. Envelope demodulation analysis of bearing based on order tracking and Empirical mode decomposition [J]. Chinese Journal of Mechanical Engineering, 2007, 43(8): 119122.
[5] 张海勇. 一种新的非平稳信号分析方法—局域波分析[J]. 电子与信息学报, 2003, 25(10): 13271333.
ZHANG Haiyong. A new method for analyzing nonstation ary signallocal wave analysis [J]. Journal of Electronics and Information Technology, 2003, 25(10): 13271333.
[6] XU Yong, ZHANG David, SONG Fengxi, et al. A method for speeding up feature extraction based on KPCA [J]. Neurocomputing, 2007, 70(46): 10561061.
[7] HUANG N E, SHEN Z, STEVEN R L, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis [J]. Proceedings of the Royal Society of London, A. 1998, 454(19): 903995.
[8] 孙晖, 朱善安. 基于自适应滤波的滚动轴承故障诊断研究[J]. 浙江大学学报:工学版, 2005, 39(11): 17461749.
SUN Hui, ZHU Shanan. Rolling bearing fault diagnosis based on adaptive filtering [J]. Journal of Zhejiang University:Engineering Science, 2005, 39(11): 17461749.
[9] RILLING G, FLANDRIN P, GONCALVES P. On empirical mode decomposition and its algorithms [C]∥IEEE EURASIP Workshop on NSIP03. Grado, Italy:IEEE, 2003: 811.
[10] SCHLGL A. A comparison of multivariate autoregressive estimators [J]. Signal Processing, 2006, 86: 24262429.
[11] CHIH W H, CHIH J L.A comparison of methods for multiclass support vector machines [J].Neural Networks, 2002, 13(2): 415425.
[12] Case Western Reserve University. Bearing data center [EB/OL]. [20080829]. http:∥www.eecs.cwru.edu/laboratory/bearing.

[1] 宁志华,何乐年,胡志成. 一种高压高可靠性开关电源控制芯片[J]. J4, 2014, 48(3): 377-383.
[2] 李林,陈家旺,顾临怡,王峰. 轴向柱塞泵/马达变量阀配流机构[J]. J4, 2014, 48(1): 29-34.
[3] 陈钊,余锋,陈婷婷. 基于日志结构的闪存均衡回收策略[J]. J4, 2014, 48(1): 92-99.
[4] 蒋湛,姚晓明,林兰芬. 基于特征自适应的本体映射方法[J]. J4, 2014, 48(1): 76-84.
[5] 陈迪仕 ,张宇,李平. 微小型无人直升机地面效应建模[J]. J4, 2014, 48(1): 154-160.
[6] 霍新新,褚金奎,韩冰峰,姚斐.  基于多个压电换能器的接口电路[J]. J4, 2013, 47(11): 2038-2045.
[7] 杨鑫,许端清,杨冰. 基于不规则性的并行计算方法[J]. J4, 2013, 47(11): 2057-2064.
[8] 王玉强,张宽地,陈晓东. 胶黏钢-混凝土组合梁的界面行为数值分析[J]. J4, 2013, 47(9): 1593-1598.
[9] 彭勇,徐小剑. 集料分布对沥青混合料劈裂强度影响数值分析[J]. J4, 2013, 47(7): 1186-1191.
[10] 崔何亮, 张丹, 施斌.  布里渊分布式传感的空间分辨率及标定方法[J]. J4, 2013, 47(7): 1232-1237.
[11] 伍晓榕,裘乐淼,张树有,孙良峰,郭传龙. 模糊语境下的复杂系统关联FMEA方法[J]. J4, 2013, 47(5): 782-789.
[12] 金波,陈诚,李伟. 具有半球形足端的六足机器人步态修正算法[J]. J4, 2013, 47(5): 768-774.
[13] 钟世英, 吴晓君, 蔡武军, 凌道盛, 蒋祝金, 王顺玉. 月面软着陆足垫水平拖曳模型试验装置研制[J]. J4, 2013, 47(3): 465-471.
[14] 袁幸,朱永生,张优云,洪军,祁文昌. 基于正反问题的滚动轴承损伤程度评估[J]. J4, 2012, 46(11): 1960-1967.
[15] 杨飞,朱株,龚小谨,刘济林. 基于三维激光雷达的动态障碍实时检测与跟踪[J]. J4, 2012, 46(9): 1565-1571.