EWT与加权多邻域粗糙集结合的旋转机械故障特征提取方法

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Abstract In the use of attribute reduction with a neighborhood rough set (NRS), the neighborhood radius was needed to be adjusted for several times iteratively.And it was not determined automatically.In order to solve this inconvenience, a feature selection method based on weighted multi neighborhood rough set (WMNRS) was proposed.Combined with the method of empirical wavelet transform (EWT) in rotating machinery, a fault feature extraction method for rotating machinery was proposed.Firstly, the vibration signal of rotating machinery with nonlinear and strong noise was reconstructed with a group of EWT’ optimal modal component selected by correlation, and a high dimensional original feature set was constructed with time domain characteristics of the reconstructed signal.Then, a feature subset was obtained from the original feature dataset by NRSin different neighborhood radius.Last, the probability of occurrence for each feature in the attribute reduction with multiple neighborhood rough sets was counted as feature weight, which was weighted with feature value as sensitive feature set.A characteristic of this method was that it can extract feature automatically in neighborhood rough sets, and the extracted features were more distinguishable.A rotor experiment shows that this method can extract the characteristics of vibration signals effectively, and the fault types of the rotor can be identified correctly according to feature vectors.It provides a theoretical base for solving the classification problem of nonlinear, strong noise, and high-dimensional fault dataset.

Key words： feature extraction probability weighted multi neighborhood rough set(WMNRS) empirical wavelet transform(EWT) rotating machinery

Received: 18 July 2018 Published: 15 December 2019

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	Articles by authors
	WU Yaochun1
	2
	ZHAO Rongzhen1
	JIN Wuyin1

Cite this article:

WU Yaochun1,2,ZHAO Rongzhen1, et al. Fault feature extraction of rotating machinery based on EWT and a weighted multi neighborhood rough set[J]. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(24): 235-242.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2019/V38/I24/235

[1]武哲, 杨绍普, 刘永强. 基于多元经验模态分解的旋转机械早期故障诊断方法[J]. 仪器仪表学报, 2016,37(02):241-248.
WU Zhe, YANG Shaopu，LIU Yongqiang. Rotating machinery early fault diagnosis method based onmultivariate empirical mode decomposition[J].Chinese Journal of Scientific Instrument, 2016,37(02):241-248.
[2]李晗,萧德云.基于数据驱动的故障诊断方法综述[J].控制与决策, 2011, 26(01): 1-9.
LI Han, XIAO Deyun. Survey on data driven fault diagnosis methods[J]. Control and Decision, 2011,26(1):1-9.
[3]李宏全, 郭兴明,郑伊能. 基于EMD和MFCC的舒张期心杂音的分类识别[J]. 振动与冲击, 2017. 36(11): 8-13.
LIHongquan，GUO Xingming，ZHENG Yineng. Classification and recognition of diastolic heart murmurs based on EMD and MFCC[J]. Journal of Vibration and Shock, 2017,36(11):8-13.
[4]谭洋波, 程进军,刘帅.基于EMD与邻域粗糙集的液体电磁阀故障诊断[J]. 计算机工程与应用, 2017. 53(12): 255-260.
TAN Yangbo, CHENG Jinjun, LIU Shuai. Liquid solenoid valve fault diagnosis based on EMD and neighborhood rough set[J]. Computer Engineering and Applications, 2017,53(12):255-260.
[5]张志刚,石晓辉,施全，等.基于改进EMD和谱峭度法滚动轴承故障特征提取[J]. 振动.测试与诊断, 2013. 33(03): 478-
482.
ZHANG Zhigang, SHI Xiaohui, Shi Quan, et al. Fault feature extraction of rolling element bearing based on improved EMD and spectral kurtosis[J]. Journal of Vibration Measurement &Diagnosis, 2013,33(03):478-482.
[6]汤健, 柴天佑, 丛秋梅,等. 基于EMD和选择性集成学习算法的磨机负荷参数软测量[J]. 自动化学报, 2014,40(9):1853-
1866.
TANG Jian, CHAI Tianyou, CONG Qiumei, et al. Soft sensor approach for modeling mill load parameters based on EMD and selective ensemble learning algorithm[J]. Acta automatica sinica, 2014,40(9):1853-1866.
[7]Jérôme Gilles. Empirical Wavelet Transform[J].Transactions on signal processing. IEEE, 2013,61(16):3999-4010.
[8] Chen Hui, Kang Jia-Xing, Chen Yuan-Chun, et al. An improved time-frequency analysis method for hydrocarbon detection based on EWT and SET[J]. Energies, 2017. 1090(10): 1-12.
[9]向玲,李媛媛.经验小波变换在旋转机械故障诊断中的应用[J]. 动力工程学报, 2015,35(12):975-981.
XIANGLing, LIYuanyuan. Application of empirical wavelet transform in fault diagnosis of rotary mechanisms[J].Journal of Chinese Society of Power Engineering, 2015,35(12):975-981.
[10]李志农, 朱明, 褚福磊, 等. 基于经验小波变换的机械故障诊断方法研究[J]. 仪器仪表学报, 2014,35(11):2423-2432.
LIZhinong, ZHU Ming, CHU Fulei, et al. Mechanical fault diagnosis method based on empirical wavelet transform[J]. Chinese Journal of Scientific Instrument,2014,35(11):2423-2432.
[11] Z P. Rough sets[J]. International Journal of Computer & Infor-mation Sciences, 1995,38(11):88-95.
[12]胡可云, 陆玉昌, 石纯一. 粗糙集理论及其应用进展. 清华大学学报 (自然科学版), 2001,41(1):64-68.
HU Keyun, LU Yuchang, SHI Chunyi. Advances in rough set theory and its applications[J]. Journal of Tsinghua University (Science and Technology), 2001,41(1):64-68.
[13]诸文智, 司刚全, 张彦斌. 采用邻域决策分辨率的特征选择算法[J]. 西安交通大学学报, 2013,47(02):20-27.
ZHU Wenzhi, SI Gangquan, ZHANG Yanbin. Feature selection algorithm based on neighborhood decision distinguishing rate[J]. Journal of Xi'an Jiaotong University, 2013,47(02):20-27.
[14]Hu Q, Yu D, Liu J, et al. Neighborhood rough set based heterogeneous feature subset selection[J]. Information Scien-ces,2008,178(18):3577-3594.
[15] Yao Liu, Yue hua Chen, Kezhu Tan,et al. Maximum relevance, minimum redundancy band selection based on neighborhood rough set for hyperspectral data classification[J]. Measurement Science and Technology, 2016,12(27):1-13.
[16]单亚峰, 汤月, 任仁, 等. 基于邻域粗糙集与支持向量极端学习机的瓦斯传感器故障诊断[J]. 传感技术学报, 2016,29(09):1400-1404.
SHAN Yafeng, TANG Yue, REN Ren, et al. Gas sensor fault diagnosis based on neighborhood rough set combined with support vector machine and extreme learning machine[J]. Chinese Journal of Sensors and Actuators, 2016,29(09):1400-
1404.
[17]安若铭, 索明亮. 邻域粗糙集在属性约简及权重计算中的应用[J].计算机工程与应用, 2016,52(07):160-165.
AN Ruoming, SUO Mingliang. Application of attributes reduc-tionand weights calculation through neighborhood rough set[J].Computer Engineering and Applications, 2016,52(07):160-165.
[18]张冬雯, 王鹏, 仇计清. 基于邻域粗糙集和蚁群优化的属性约简算法[J]. 河北科技大学学报, 2011,32(05):403-408.
ZHANG Dongwen, WANG Peng, Qiu Jiqing. Approach to feature selection based on neighborhood roughset and ant colony optimization[J]. Journal of Hebei University of Science andTechnology,2011,32(05):403-408.
[19]陈铁桥, 柳稼航, 朱锋,等. 适用于遥感分类的多邻域粗糙集加权特征提取方法[J].武汉大学学报（信息科学版）, 2018,43(2):311-317.
CHEN Tieqiao, LIU Jiahang, ZHU Feng, et al. A novel multi-radius neighborhood rough set weighted feature extraction method for remote sensing imageclassification[J]. Geomatics and Information Science of Wuhan University,
2018,43(2):311-317.
[20]霍天龙, 赵荣珍, 胡宝权. 基于熵带法与PSO优化的SVM转子故障诊断[J]. 振动.测试与诊断, 2011,31(03):279-284
HUO Tianlong, ZHAO Rongzhen, HU Baoquan. Fault diagno-
sisfor rotor systems based on entropy band method and support vector machine optimized by PSO[J]. Journal of Vibration, Measurement & Diagnosis, 2011,31(03):279-284.
[21] 蔡艳平,李艾华,王涛,等.基于EMD-Wigner-Ville的内燃机振动时频分析[J]. 振动工程学报, 2010, 23(4):430-437.
Cai Yanping, Li Aihua, Wang Tao, et al. I.C engine vibration time-frequency analysis based on EMD-Wigner-Ville[J].Journal of Vibration Engineering，2010,23(4):430-437.
[22]欧璐,于德介.基于监督拉普拉斯分值和主元分析的滚动轴承故障诊断[J]. 机械工程学报, 2014, 50(5):88-94.
OU Lu,YU Dejie. Rolling bearing fault diagnosis based on sup-ervised laplaian score and principal component analysis[J]. Journal of Mechanical Engineering, 2014, 50(5):88-94.
[23] 童超,郭鹏.基于特征选择和BP神经网络的风电机组故障分类监测研究[J]. 动力工程学报, 2014, 34(4):313-317.
TONG Chao, GUO Peng. Wind turbine fault classification base-d on BP neural network and feature selection algorithm[J].
Chinese Journal of Power Engineering, 2014, 34(4):313-317.
[24] 王新,闫文源.基于变分模态分解和SVM的滚动轴承故障诊断[J].振动与冲击, 2017, 36(18):252-256.
WANG Xin, YAN Wenyuan. Fault diagnosis of roller bearings based on the variational mode decomposition and SVM[J]. Journal of Vibration and Shock,2017, 36(18):252-256.