您的位置:山东大学 -> 科技期刊社 -> 《山东大学学报(工学版)》

山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (3): 89-95.doi: 10.6040/j.issn.1672-3961.0.2016.270

• • 上一篇    下一篇

基于改进EMD和数据分箱的轴承内圈故障特征提取方法

于青民1,李晓磊1*,翟勇2   

  1. 1. 山东大学控制科学与工程学院, 山东 济南 250061;2. 东北电力大学机械工程学院, 吉林 吉林 132012
  • 收稿日期:2016-07-18 出版日期:2017-06-20 发布日期:2016-07-18
  • 通讯作者: 李晓磊(1973— ),男,山东济南人,副教授,博士,主要研究方向大数据分析,复杂系统建模与优化. E-mail: qylxl@sdu.edu.cn E-mail:minge09@126.com
  • 作者简介:于青民(1991— ),女,山东招远人,硕士研究生,主要研究方向为基于大数据分析的设备故障诊断. E-mail: minge09@126.com

Feature extraction method of rolling bearing inner ring in wind turbine based on improved EMD and feature box

YU Qingmin1, LI Xiaolei1*, ZHAI Yong2   

  1. 1. School of Control Science and Engineering, Shandong University, Jinan 250061, Shandong, China;
    2. School of Mechanical Engineering, Northeast Dianli University, Jilin 132012, Jilin, China
  • Received:2016-07-18 Online:2017-06-20 Published:2016-07-18

摘要: 为解决直驱风力发电机主轴后轴承内圈轻微损伤故障诊断问题,针对实际工程中振动信号的复杂特性,提出一种基于改进经验模态分解(empirical mode decomposition, EMD)和数据分箱的特征提取算法。将信号进行改进经验模态分解,得到一系列平稳的本征模函数(intrinsic mode function, IMF)。对分解后的信号提取均值、方差等幅域参数特征,并根据参数有效性选择部分参数组成特征矩阵。选用等宽分箱方法,用箱内数据均值代替箱体数据,将特征矩阵进行平滑处理。经验证,该方法能准确提取实际工程信号中的有效特征,并从特征选择的角度较好解决了分类器代价敏感问题,减少了机器学习模型的过拟合现象。

关键词: 改进经验模态分解, 数据分箱, 特征提取, 故障诊断, 代价敏感问题, 滚动轴承内圈

Abstract: According to the characteristics of vibration signal of rolling bearing inner ring in direct-driven wind turbine, a new method of fault diagnosis by improved empirical mode decomposition(EMD)and feature box was put forward. The original signal was decomposed by improved EMD to get a finite number of stationary intrinsic mode functions(IMFs). The characteristics of amplitude domain parameters such as mean and variance were extracted, which were turned into feature matrix chose by effectiveness. To perform data smoothing processing, The feature matrix was divided into boxes and replaced by means of data in each box. Examples showed that the feature matrix, which was divided into boxes finally, could effectively extract the fault feature of rolling bearing, and reduce the over fitting of the machine learning model.

Key words: data sub box, bearing inner ring, feature extraction, fault diagnosis, improved EMD, cost sensitive

中图分类号: 

  • TH17
[1] 蒋东翔,洪良友,黄乾,等. 风力机状态监测与故障诊断技术研究[J].电网与清洁能源,2008,24(3):40-44. JIANG Dongxiang, HONG Liangyou, HUANG Qian, et al. Condition monitoring and fault diagnostic techniques for wind turbine[J].Power System and Clean Energy, 2008, 24(3):40-44.
[2] 苏连成,李兴林. 中国北方地区风电轴承故障调查与分析[J].轴承,2013(11):59-62. SU Lianchen, LI Xinglin. Investigation and analysis of fault for wind turbine bearings in northern China[J]. Bearing, 2013(11):59-62.
[3] WALFORD A. Christopher.Wind turbine reliability:understanding and minimizing wind turbine operation and maintenance costs[R].California, USA: Sandia Corporation, 2006.
[4] 吴娜,孙丽玲,杨普. 风力机状态监测与故障诊断技术研究[J].华北水利水电学院学报,2012,33(2):86-90. WU Na, SUN Liling, YANG Pu. Research on wind turbine condition monitoring and fault diagnosis[J].Journal of North China Institute of Water Conservancy and Hydroelectric Power, 2012, 33(2):86-90.
[5] 周福昌,陈进,何俊, 等. 循环平稳信号处理在机械设备故障诊断中的应用综述[J].振动与冲击, 2006, 25(5):148-152. ZHOU Fuchang, CHEN Jin, HE Jun, et al. Survey of the application of cyclostationary signal processing in machinery fault diagnosis[J].Journal of Vibration and Shock, 2006, 25(5):148-152.
[6] 赵志宏,杨绍普.基于小波包变换与样本熵的滚动轴承故障诊断[J].振动、测试与诊断,2012,32(4):640-644. ZHAO Zhihong, YANG Shaopu. Roller bearing fault diagnosis based on wavelet packet transform and sample entropy[J]. Journal of Vibration, Measurement & Diagnosis, 2012, 32(4):640-644.
[7] 冯辅周,司爱威,饶国强,等.基于小波相关排列熵的轴承早期故障诊断技术[J]. 机械工程学报,2012,48(13):73-79. FENG Fuzhou,SI Aiwei, RAO Guoqiang, et al. Early fault diagnosis technology for bearing based on wavelet correlation permutation entropy[J]. Journal of Mechanical Engineering, 2012, 48(13):73-79.
[8] 于德介,陈淼峰,程军圣,等.一种基于经验模式分解与支持向量机的转子故障诊断方法[J]. 中国电机工程学报,2006,26(16):162-167. YU Dejie, CHEN Miaofeng, CHENG Junsheng, et al. A fault diagnosis approach for rotor systems based on empirical mode decomposition method and support vector machines[J]. Proceedings of the CSEE, 2006, 26(16):162-167.
[9] 苏文胜,王奉涛,张志新,等.EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J]. 振动与冲击,2010,29(3):18-21. SU Wensheng, WANG Fengtao, ZHANG Zhixin, et al. The application of EMD and spectral kurtosis method in the early fault diagnosis of rolling bearing[J]. Journal of Vibration and Shock, 2010, 29(3):18-21.
[10] 张超,陈建军,徐亚兰.基于EMD分解和奇异值差分谱理论的轴承故障诊断方法[J]. 振动工程学报,2011,24(5):539-545. ZHANG Chao, CHEN Jianjun, XU Yalan. A bearing fault diagnosis method based on EMD and difference spectrum theory of singular value[J]. Journal of Vibration Engineering, 2011, 24(5):539-545.
[11] 胡爱军,马万里,唐贵基. 基于集成经验模态分解和峭度准则的滚动轴承故障特征提取方法[J]. 中国电机工程学报,2012, 32(11):106-111. HU Aijun, MA Wanli, TANG Guiji. Rolling bearing fault feature extraction method based on ensemble empirical mode decomposition and Kurtosis criterion[J].Proceedings of the CSEE, 2012, 32(11):106-111.
[12] HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis[J]. Proceedings of the Royal Society A, 1998, 454(1971):903-995.
[13] HUANG D J, ZHAO J P, SUN J L. Practical implementation of Hilbert-Huang transform algorithm[J]. Acta Oceanologica Sinica, 2003, 22(1):1-14.
[14] 李航. 统计学习方法[M]. 北京:清华大学出版社, 2012:18-20.
[15] 付忠良. 多分类问题代价敏感AdaBoost算法[J].自动化学报, 2011, 37(8):973-983. FU Zhongliang. Cost-sensitive AdaBoost algorithm for multi-class classification problems[J]. Acta Automatica Sinica, 2011, 37(8):973-983.
[16] 刘金福, 于达仁, 胡清华, 等. 基于加权粗糙集的代价敏感故障诊断方法[J]. 中国电机工程学报, 2007, 27(23):93-99. LIU Jinfu, YU Daren, HU Qinghua, et al. Cost-sensitive fault diagnosis based on weighted rough sets[J]. Proceedings of the CSEE, 2007, 27(23):93-99.
[17] 韩家炜, MICHELINE Kamber. 数据挖掘:概念与技术[M]. 范明, 译. 北京:机械工业出版社, 2001:70-71.
[18] 王洁松, 张小飞. 基于特征匹配和分箱技术的FCM算法研究[J]. 南通航运职业技术学院学报, 2011, 10(3):56-59. WANG Jiesong, ZHANG Xiaofei. AFCM algorithm based on character mMatching and bBinning[J]. Journal of Nantong Vocational & Technical Shipping College, 2011, 10(3):56-59.
[19] 傅涛,孙文静,孙亚民. 基于分箱统计的FCM算法及其在网络入侵检测中的应用[J]. 计算机科学,2008,35(4):36-39. FU Tao, SUN Wenjing, SUN Yamin. Algorithm based on box-FCM statistics and its application in network intrusion detection[J].Computer Science, 2008, 35(4):36-39.
[20] 张明锦,王明伟. 基于数据分箱的CARS方法用于基因表达谱的特征筛选[J]. 计算机与应用化学,2015,32(8):1004-1006. ZHANG Mingjin, WANG Mingwei. Use of binning-based CARS method for feature selection from gene expression data[J].Computers and Applied Chemistry, 2015, 32(8):1004-1006.
[21] 袁朝庆, 赵丹,余亚辉. 基于经验模态分解法和时域幅值参数识别结构损伤程度[J]. 无损检测,2008, 30(2):84-86. YUAN Zhaoqing, ZHAO Dan, YU Yahui. Identifying the damage degree of structure based on empirical mode decomposition and parameters in time domain amplitude[J]. Nondestructive Testing, 2008, 30(2):84-86.
[22] 张学工. 关于统计学习理论与支持向量机[J]. 自动化学报,2000, 26(1):32-42. ZHANG Xuegong. Introduction to statistical learning theory and support vector machines[J]. Acta Automatica Sinica, 2000, 26(1):32-42.
[23] 张周锁,李凌均,何正嘉. 基于支持向量机的机械故障诊断方法研究[J]. 西安交通大学学报,2002,36(12):1303-1306. ZHANG Zhousuo, LI Lingun, HE Zhengjia. Research on diagnosis method of machinery fault based on support vector machine[J].Journal of Xi'an Jiaotong University, 2002, 36(12):1303-1306.
[1] 王国新,陈凤东,刘国栋. 基于彩色伪随机编码结构光特征提取方法[J]. 山东大学学报(工学版), 2018, 48(5): 55-60.
[2] 程鑫,张林,胡业发,陈强,梁典. 基于电流特性的主动磁轴承电磁线圈故障诊断[J]. 山东大学学报(工学版), 2018, 48(4): 94-101.
[3] 叶子云,杨金锋. 一种基于加权图模型的手指静脉识别方法[J]. 山东大学学报(工学版), 2018, 48(3): 103-109.
[4] 程鑫,刘晗,王博,梁典,陈强. 基于双核处理器的主动磁悬浮轴承容错控制架构[J]. 山东大学学报(工学版), 2018, 48(2): 72-80.
[5] 张振月,李斐,江铭炎. 基于低秩表示投影的无监督人脸特征提取[J]. 山东大学学报(工学版), 2018, 48(1): 15-20.
[6] 王秀青,曾慧,解飞,吕峰. 基于Spiking神经网络的机械臂故障诊断[J]. 山东大学学报(工学版), 2017, 47(5): 15-21.
[7] 宋洋,钟麦英. 基于改进距离相似度的故障可分离性分析方法[J]. 山东大学学报(工学版), 2017, 47(5): 103-109.
[8] 李炜,王可宏,曹慧超. 基于新型ESF的一类非线性系统故障滤波方法[J]. 山东大学学报(工学版), 2017, 47(5): 7-14.
[9] 毛海杰,李炜,王可宏,冯小林. 基于自抗扰的多电机转速同步系统传感器故障切换容错策略[J]. 山东大学学报(工学版), 2017, 47(5): 64-70.
[10] 邱路,叶银忠,姜春娣. 基于小波奇异熵和SOM神经网络的微电网系统故障诊断[J]. 山东大学学报(工学版), 2017, 47(5): 118-122.
[11] 谢晓龙,姜斌,刘剑慰,蒋银行. 基于滑模观测器的异步电动机速度传感器故障诊断及容错控制[J]. 山东大学学报(工学版), 2017, 47(5): 210-214.
[12] 王梦园,张雄,马亮,彭开香. 基于因果拓扑图的工业过程故障诊断[J]. 山东大学学报(工学版), 2017, 47(5): 187-194.
[13] 孙源呈,姚利娜. 不确定奇异随机分布系统的故障诊断和容错控制[J]. 山东大学学报(工学版), 2017, 47(5): 238-245.
[14] 李明虎,李钢,钟麦英. 动态核主元分析在无人机故障诊断中的应用[J]. 山东大学学报(工学版), 2017, 47(5): 215-222.
[15] 刘卓,王天真,汤天浩,冯页帆,姚君琦,高迪驹. 一种多电平逆变器故障诊断与容错控制策略[J]. 山东大学学报(工学版), 2017, 47(5): 229-237.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!