基于ELMD能量熵与AFSA-SVM的行星齿轮箱关键部件故障诊断研究
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摘要
针对行星齿轮箱振动信号复杂时变调质特点使其"难表征",致使据此构建的状态辨识模型精度低的问题,提出一种基于总体局部均值分解(Ensemble local mean decomposition,ELMD)的能量熵与人工鱼群算法(Artificial fish swarm algorithm,AFSA)寻找支持向量机(Support vector machine,SVM)最优核函数系数组合的行星齿轮箱关键部件的状态辨识方法。首先,利用ELMD分解经形态平均滤波的行星齿轮箱关键部件的振动信号来获取若干窄带乘积函数(Product function,PF)。然后,计算其能量熵来构建高维特征向量集。最后,将其作为输入,通过训练学习建立AFSA优化SVM的行星齿轮箱关键部件状态辨识模型。实验结果表明,所提方法能凸显原信号中的有效故障成份,提高了模型的状态辨识精度。
Aiming at the problem of complex modulation characteristics of vibration signals of the planetary gearbox make the state identification model low accuracy,a state identification method for key components of planetary gearbox based on combinations of ensemble local mean decomposition( ELMD) energy entropy and artificial fish swarm algorithm finding support vector machine( AFSA-SVM) optimal kernel function coefficient is proposed. To begin,a number of narrow-band product function( PF) from vibration signals are obtained by ELMD with after morphological average filter. Then the high dimensional feature vector set is built by calculating the energy entropy of the above PF. At last,the fault diagnosis model is developed based on AFSA-SVM algorithm,in which the extracted fault features are employed as inputs. The experimental results show that the proposed method can show the fault component of the original signal with effectively. It has the state identification accurate of the model is greatly improved.
Aiming at the problem of complex modulation characteristics of vibration signals of the planetary gearbox make the state identification model low accuracy,a state identification method for key components of planetary gearbox based on combinations of ensemble local mean decomposition( ELMD) energy entropy and artificial fish swarm algorithm finding support vector machine( AFSA-SVM) optimal kernel function coefficient is proposed. To begin,a number of narrow-band product function( PF) from vibration signals are obtained by ELMD with after morphological average filter. Then the high dimensional feature vector set is built by calculating the energy entropy of the above PF. At last,the fault diagnosis model is developed based on AFSA-SVM algorithm,in which the extracted fault features are employed as inputs. The experimental results show that the proposed method can show the fault component of the original signal with effectively. It has the state identification accurate of the model is greatly improved.
引文
[1]LIU J,SHAO Y,ZHU W D.A new model for the relationship between vibration characteristics caused by the time-varying contact stiffness of a deep groove ball bearing and defect sizes[J].Journal of Tribology,2015,137(3):031101.
[2]冯志鹏,褚福磊,左明健.行星齿轮箱故障振动故障诊断方法[M].北京:科学技术出版社,2015:2-8.
[3]李康强,冯志鹏.基于生成微分方程的行星齿轮箱故障振动信号解调分析[J].振动与冲击,2017,36(8):9-15.
[4]陈鹏飞,陈卫,高星伟,等.LMD和支持向量机相结合的齿轮毂故障诊断方法[J].机械科学与技术,2015,34(10):1599-1603.
[5]郑近德,潘海洋,张俊,等.APEEMD及其在转子碰摩故障诊断中的应用[J].振动、测试与诊断,2016,36(2):257-263.
[6]何志坚,周志雄.基于ELMD的样本熵及Boosting-SVM的滚动轴承故障诊断[J].振动与冲击,2016,35(18):190-195.
[7]YANG Yu,CHENG Junsheng,ZHANG Kang.An ensemble localmeans decomposition method and its application to local rubimpactfault diagnosis of the rotor systems[J].Measurement,2012,45(3):561-570.
[8]王建国,陈帅,张超.噪声参数最优ELMD与LS-SVM在轴承故障诊断中的应用与研究[J].振动与冲击,2017,36(5):72-78.
[9]程军圣,张亢,杨宇.基于噪声辅助分析的总体局部均值分解方法[J].机械工程学报,2011,47(3):55-62.
[10]陈伟根,谢波,龙震泽,等.基于小波包能量熵的油纸绝缘气隙放电阶段识别[J].中国电机工程学报,2016,36(2):563-569.
[11]朱文静.人工鱼群算法优化的SVM在语音识别中的应用[D].太原:太原理工大学,2016:29-44.
[12]高屹宇.基于鱼群算法优化支持向量机网络安全态势评估模型[D].兰州:兰州大学,2016:22-31.
[13]秦波,刘永亮,王建国,等.基于相空间奇异值分解与AFSASVM的齿轮故障诊断方法[J].组合机床与自动化加工技术,2016(8):67-70.
[2]冯志鹏,褚福磊,左明健.行星齿轮箱故障振动故障诊断方法[M].北京:科学技术出版社,2015:2-8.
[3]李康强,冯志鹏.基于生成微分方程的行星齿轮箱故障振动信号解调分析[J].振动与冲击,2017,36(8):9-15.
[4]陈鹏飞,陈卫,高星伟,等.LMD和支持向量机相结合的齿轮毂故障诊断方法[J].机械科学与技术,2015,34(10):1599-1603.
[5]郑近德,潘海洋,张俊,等.APEEMD及其在转子碰摩故障诊断中的应用[J].振动、测试与诊断,2016,36(2):257-263.
[6]何志坚,周志雄.基于ELMD的样本熵及Boosting-SVM的滚动轴承故障诊断[J].振动与冲击,2016,35(18):190-195.
[7]YANG Yu,CHENG Junsheng,ZHANG Kang.An ensemble localmeans decomposition method and its application to local rubimpactfault diagnosis of the rotor systems[J].Measurement,2012,45(3):561-570.
[8]王建国,陈帅,张超.噪声参数最优ELMD与LS-SVM在轴承故障诊断中的应用与研究[J].振动与冲击,2017,36(5):72-78.
[9]程军圣,张亢,杨宇.基于噪声辅助分析的总体局部均值分解方法[J].机械工程学报,2011,47(3):55-62.
[10]陈伟根,谢波,龙震泽,等.基于小波包能量熵的油纸绝缘气隙放电阶段识别[J].中国电机工程学报,2016,36(2):563-569.
[11]朱文静.人工鱼群算法优化的SVM在语音识别中的应用[D].太原:太原理工大学,2016:29-44.
[12]高屹宇.基于鱼群算法优化支持向量机网络安全态势评估模型[D].兰州:兰州大学,2016:22-31.
[13]秦波,刘永亮,王建国,等.基于相空间奇异值分解与AFSASVM的齿轮故障诊断方法[J].组合机床与自动化加工技术,2016(8):67-70.