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| Wind Power Gear Box Fault Diagnosis Based on DEMD Local Frequency Entropy and SVM |
| MENG Zong1,2,LIU Dong1,YUE Jian-hui1,ZHAN Xu-yang1,MA Zhao1,LI Jing1 |
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Abstract In order to extract the constituent of useful information from the nonlinear and non-stationary wind gearbox fault signal. A new approach for wind power gear box fault diagnosis based on the combination of differential-based empirical mode decomposition(DEMD), local frequency entropy and support vector machine(SVM) is proposed. Firstly, fault vibration signal is filtered with adaptive multi-scale mathematical morphology. Then mechanical vibration signal is decomposed with DEMD to obtain a certain number of intrinsic mode functions(IMF). Then the local time-frequency entropy of the IMF components are calculated and used as the eigenvectors of SVM. Finally, the eigenvectors are put into SVM to identify the state of the wind power gear box. The experimental results show that the method based on the combination of DEMD, local time-frequency entropy and SVM can be used to recognize and classify rolling bearing fault signals accurately and effectively.
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Received: 03 August 2015
Published: 16 June 2017
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Corresponding Authors:
Zong MENG
E-mail: mzysu@ysu.edu.cn
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［1］周安美,于德介,吴雪明,等.基于本体的大型风力发电机组故障智能诊断研究［J］.中国机械工程,2012,23 (17):2075-2080.
［2］Tang B P, Liu W Y, Tao S. Wind Turbine Fault Diagnosis Based on Morlet Wavelet Transformation and Wigne-ville Distribution［J］.Renewable Energy,2010,35(12):2862-2866.
［3］陈雪峰,李继猛,程航,等.风力发电机状态监测和故障诊断技术的研究与进展［J］.机械工程学报, 2011, 47(9): 45-52.
［4］刘德顺,戴巨川,胡燕平,等.现代大型风电机组现状与发展趋势［J］.中国机械工程,2013,24(1):125-135.
［5］严如强,钱宇宁,胡世杰,等.基于小波域平稳子空间分析的风力发电机齿轮箱故障诊断［J］.机械工程学报,2014, 50(11):9-16.
［6］沈路,周晓军,张文斌,等.广义数学形态滤波器旋转机械振动信号降噪［J］.振动与冲击,2009, 28(9):70-73.
［7］张弦,王宏力.进化小波消噪方法及其在滚动轴承故障诊断中的应用［J］.机械工程学报,2010,46 (15):76-81.
［8］景蓓蓓,李鸿光.基于微分的经验模式方法在转子裂纹和碰摩故障诊断中的应用［J］.噪声与振动控制, 2009, 29(5):66-69.
［9］孟宗,闫晓丽. 基于微分经验模式分解和隐马尔科夫模型的滚动轴承故障诊断方法［J］. 计量学报, 2015, 36(5): 482-486.
［10］于德介,张嵬,程军圣,等.基于EMD的时频熵在齿轮故障诊断中的应用［J］.振动与冲击,2005,24(5):26-29.
［11］孟宗,李姗姗,王亚超. 基于LMD和局域时频熵的旋转机械故障诊断方法［J］. 计量学报, 2015, 36(1): 77-81.
［12］Vapnik V N. Statistical Learning Theory［M］.New York: Wiley, 1998.
［13］何群,赵文爽,江国乾,等. 基于EEMD与AR建模的风电场风速预测［J］. 计量学报, 2015, 36(2): 181-186. |
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2017, Vol. 38 
