SVM Rotor Vibration Fault Diagnosis Based on EEMD Permutation Entropy

doi:10.3969/j.issn.1000-7229.2016.01.014

Abstract

Abstract:

The accurate identification of the fault conditions of steam turbine rotor has been the research focus in the field of engineering. In the process of fault diagnosis by using support vector machine (SVM), extracting the signal characteristic parameters, which can clearly distinguish different fault signals to construct high-quality samples, plays a significant role in improving the classification accuracy of SVM model. To solve these problems, we propose a multiple fault diagnosis method for steam turbine rotor based on ensemble empirical mode decomposition (EEMD), permutation entropy and SVM. Firstly, this method applies directed acyclic graph to establish multiple faults diagnosis model, and uses EEMD to decompose the vibration signals into single and unmixed IMF components. Then, the permutation entropy of IMF component, which is very sensitive to the changes in vibration signal, is calculated as eigenvectors, and applied in directed acyclic graph SVM for multiple fault state recognition. The experimental results show that this method can realize the multiple faults diagnosis of turbine rotor vibration. Meanwhile, compared with the extracted eigenvectors based on EEMD energy method, the experiment proves that this method has more accurate recognition rate.

Key words: EEMD (ensemble empirical mode decomposition), permutation entropy, SVM (support vector machine), rotor, fault diagnosis

CLC Number:

TM 62
TK 267

HAN Zhonghe，JIAO Hongchao，ZHU Xiaoxun，WANG Zhi. SVM Rotor Vibration Fault Diagnosis Based on EEMD Permutation Entropy[J]. Electric Power Construction, 2016, 37(1): 92-96.

References

［1］WU Z，HUANG N E. Ensemble empirical mode decomposition: a noise assisted data analysis method［J］. Advances in Adaptive Data Analysis，2009，1(1)：1-41.
［2］BANDT C, POMPE B. Permutation entropy: a natural complexity measure for time series［J］. Physical review letters, 2002, 88(17): 174102.
［3］马千里,卞春华.改进排列熵方法及其在心率变异复杂度分析中的应用［J］.中国组织工程研究与临床康复，2011，14(52)：9781-9785.
MA Qianli, BIAN Chunhua. Application of modified permutation entropy in heart rate variability analysis［J］. Journal of Clinical Rehabilitative Tissue Engineering Research, 2010, 52(14):9781-9785.
［4］YAN R Q, LIU Y B , GAO R X. Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines［J］.Mechanical Systems and Signal Processing, 2012, 29: 474-484.
［5］石国良，李晓，金炜东，等.基于排列组合熵的高速列车走行部故障分析［J］.计算机应用研究，2014，31(12)：3625-3627.
SHI Guoliang, LI Xiao, JIN Weidong, et al. Fault analysis of high-speed train based on permutation entropy［J］. Application Research of Computers, 2014，31(12)：3625-3627.
［6］郑近德,程军圣,杨宇.多尺度排列熵及其在滚动轴承故障诊断中的应用［J］.中国机械工程, 2013，24(19): 2641-2646.
ZHENG Jinde, CHENG Junsheng, YANG Yu. Multi-scale permutation entropy and its application to rolling bearing fault diagnosis［J］. China Mechanical Engineering, 2013, 24(19): 2641-2646.
［7］陈艳娜.基于EEMD的旋转机械故障诊断方法研究［D］.哈尔滨: 哈尔滨理工大学，2013.
CHEN Yanna. Research on fault diagnosis of rotating machinery method based on EEMD algorithm［D］. Harbin: Harbin University of Science and Technology, 2013.
［8］卢艳超，温卫宁，赵彪，等.基于EEMD-ARMA模型的电网工程导线价格预测［J］.电力建设，2013，34(11)：39-44.
LU Yanchao, WEN Weining, ZHAO Biao, et al. Wire price forecasting of grid project based on EEMD and ARMA［J］. Electric Power Construction, 2013, 34(11): 39-44.
［9］袁明，罗志增.基于排列组合熵和聚类分析的SEMG识别方法［J］.华中科技大学学报：自然科学版，2011，39(Ⅱ)：107-109.
YUAN Ming, LUO Zhizeng. SEMG recognition based on permutation entroy and clustering analysis［J］. Huazhong University of Science Technology：Natural Science Edition, 2011, 39(Ⅱ): 107-109.
［10］朱霄珣.基于支持向量机的旋转机械故障诊断与预测方法研究［D］.保定：华北电力大学,2013.
ZHU Xiaoxun. Research on rotating machine fault diagnosis and prediction method based on support vector machine ［D］. Baoding: North China Electric Power University, 2013.
［11］王绵斌，李欢欢，谭忠富，等.基于支持向量机方法的输电工程造价指标评估体系［J］.电力建设，2014，35(3)：102-106.
WANG Mianbin, LI Huanhuan, TAN Zhongfu, et al. Index evaluation system of power transmission project cost based on support vector machine method［J］. Electric Power Construction，2014，35(3)：102-106.
［12］韩中合,翟新杰,付士鹏,等.基于SVM的旋转机械振动故障诊断模型及系统研究［J］.煤矿机械, 2015，36(1): 286-288.
HAN Zhonghe, ZHAI Xinjie, FU Shipeng, et al. Research on diagnostic mode and system of rotation machine vibration fault based on SVM［J］. Coal Mine Machinery, 2015, 36(1): 286-288.

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