Fault Prediction for Hydraulic Pump Based on EEMD and SVM

Abstract

Abstract:

The performance of hydraulic pump will directly affect the entire hydraulic system, so it is essential for its condition monitoring and fault prediction. The vibration signals for hydraulic pump were collected and a kind of method was presented,which can get fault feature based on EEMD and smoothed energy operation separation. Wavelet packet was used to decompose energy of frequency area in order to get fault feature vectors. On the basis of studying the principles of SVM regression estimate, a prediction model was built,which was based on wavelet packet theory and SVM. The model were validated through the historical data of the hydraulic pump, and the experimental results show that the SVM regression model and fault mapping model can predict the faults effectively.

Key words: ensemble empirical mode decomposition(EEMD), energy operator, wavelet packet, support vector machine(SVM), hydraulic pump

摘要：

液压泵的性能直接影响整个液压系统的正常工作,为此需要对其进行状态监测和故障预测。采集液压泵的振动信号,运用集总经验模式分解(EEMD)和平滑能量算子解调相结合的方法进行包络解调;采取小波包分析方法得到了故障特征向量;在研究支持向量机回归估计基本原理的基础上,建立了小波包分解和支持向量机相结合的预测模型。采用液压泵历史数据对模型进行了验证,结果表明,基于支持向量机的预测模型和故障映射模型可以有效地对液压泵进行故障预测。

关键词: 集总经验模式分解(EEMD), 能量算子, 小波包, 支持向量机, 液压泵

CLC Number:

TH165
TN911

TIAN Hai-Lei, LI Hong-Ru, HU Bao-Hua. Fault Prediction for Hydraulic Pump Based on EEMD and SVM[J]. China Mechanical Engineering, 2013, 24(07): 926-931.

田海雷, 李洪儒, 许葆华. 基于集总经验模式分解和支持向量机的液压泵故障预测研究[J]. 中国机械工程, 2013, 24(07): 926-931.

References

[1]刘思远. 信息融合和贝叶斯网络集成的故障诊断理论方法及实验研究[D].秦皇岛：燕山大学,2010.
[2]吴虎胜,吕建新,吴庐山,等.基于EMD和SVM的柴油机气阀机构故障诊断[J].中国机械工程,2010,21(22):2710-2713.
Wu Husheng, Lü Jianxin, Wu Lushan, et al. Fault Diagnosis for Diesel Valve Train Based on SVM and EMD[J]. China Mechanical Engineering,2010,21(22):2710-2713.
[3]王翔，王仲生. 基于改进Hilbert-Huang变换的转子碰摩故障诊断[J]. 中国机械工程,2011,22(24):2937-2940.
Wang Xiang, Wang Zhongsheng. Incipient Rub-impact Fault Diagnosis of Rotor System Using Improved HHT[J]. China Mechanical Engineering, 2011,22(24):2937-2940.
[4]梁旭,李行善,张磊,等.支持视情维修的故障预测技术研究[J].测控技术,2007,26(6):5-8.
Liang Xu, Li Xingshan, Zhang Lei,et al. Survey of Fault Prognostics Supporting Condition Based Maintenance[J].Measurement & Control Technology, 2007,26(6):5-8.
[5]胡良谋,曹克强,王文栋,等.基于SVR的非线性系统故障诊断研究[J].机械科学与技术, 2010, 29(2):225-228.
Hu Liangmou,Cao Keqiang, Wang Wendong, et al.Fault Diagnosis of Nonlinear Systems Based on Support Vector Regression[J]. Mechanical Science and Technology for Aerospace Engineering, 2010, 29(2):225-228.
[6]林近山.基于EEMD和Hilbert变换的齿轮箱故障诊断[J].机械传动,2010,34(5):62-65.
Lin Jinshan. Gearbox Fault Diagnosis Based on EEMD and Hilbert Transform[J]. Journal of Mechanical Transmission, 2010,34(5):62-65.
[7]鞠平华，秦树人，赵玲.基于LMD的能量算子解调方法及其在故障特征信号提取中的应用[J].振动与冲击,2011,30(2):1-4.
Ju Pinghua, Qin Shuren, Zhao Ling. Energy Operator Demodulating Approach Based on LMD and Its Application in Extracting Characteristics of a Fault Signal[J].Journal of Vibration and Shock, 2011,30(2):1-4.
[8]盛兆顺，尹琦岭．设备状态监测与故障诊断技术及应用[M]．北京：化学工业出版社，2004.
[9]王冬云,张文志,张建刚.小波包能量谱在滚动轴承故障诊断中的应用[J].轴承,2010(11):32-36.
Wang Dongyun, Zhang Wenzhi, Zhang Jiangang. Application of Wavelet Packet Energy Spectrum in Rolling Bearing Fault Diagnosis[J]. Bearing, 2010(11):32-36.
[10]Chen Bojun, Chang Mingwei, Lin Chijin. Load Forecasting Using Support Vector Machines[J]. IEEE Transactions on Power Systems, 2004,19(4):1821-1830.
[11]吉训生. 基于偏最小二乘支持向量机的短期电力负荷预测方法研究[J]. 电力系统保护与控制,2010,38(23):55-59.
Ji Xunsheng. Short-term Power Load Forecasting on Partial Least Square Support Vector Machine[J]. Power System Protection and Contorl, 2010,38(23):55-59.
[12]Chang Yanwei, Wang Yaocai, Liu Tao, et al. Fault Diagnosis of a Mine Hoist Using PCA and SVM Techniques[J]. Journal of China University of Mining & Technology, 2008, 18(3):327-331.

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