基于DEMD的高压隔膜泵单向阀早期故障诊断
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摘要
针对高压隔膜泵单向阀的早期故障特征提取困难的问题,提出基于微分经验模态分解(differential empirical mode decomposition,简称DEMD)的高压隔膜泵单向阀早期故障诊断方法。首先,对振动信号进行微分运算,提高高频成分的振幅比,使微弱高频成分在后续分解中更易提取;其次,对得到的新信号进行经验模态分解(empirical mode decomposition,简称EMD),并将分解后的本征模函数(intrinsic mode function,简称IMF)分量信号进行积分还原;最后,计算分量信号与原振动信号的Kullback-Leibler散度(Kullback-Leibler divergence,简称K-L散度)值,选取K-L散度值较小的分量信号进行重构,并利用Hilbert边际谱对重构信号进行瞬时频谱分析,以提取故障振动信号的特征。仿真与工程实验分析表明,该方法能够较好地提取出单向阀早期故障特征信息。
This paper presents an early fault diagnosis method for the high-pressure diaphragm pump check valve based on the differential empirical mode decomposition.First,the differential transformation of the vibration signals is derived to increase the amplitude ratio of the high-frequency component for easy extraction in the subsequent decomposition.Further,empirical mode decomposed(EMD)is applied on the signals and the intrinsic mode functions obtained from EMD are restored by integral.Finally,the components with smaller K-L divergence with the original signals is selected to reconstruct,and the instantaneous frequency spectrum of reconstructed signals is analyzed by Hilbert marginal spectrum to extract the characteristics of the fault vibration signals.Simulation and engineering experimental analysis show that the proposed method can extract the fault feature information better.
This paper presents an early fault diagnosis method for the high-pressure diaphragm pump check valve based on the differential empirical mode decomposition.First,the differential transformation of the vibration signals is derived to increase the amplitude ratio of the high-frequency component for easy extraction in the subsequent decomposition.Further,empirical mode decomposed(EMD)is applied on the signals and the intrinsic mode functions obtained from EMD are restored by integral.Finally,the components with smaller K-L divergence with the original signals is selected to reconstruct,and the instantaneous frequency spectrum of reconstructed signals is analyzed by Hilbert marginal spectrum to extract the characteristics of the fault vibration signals.Simulation and engineering experimental analysis show that the proposed method can extract the fault feature information better.
引文
[1]李娟,周东华,司小胜,等.微小故障诊断方法综述[J].自动化学报,2012,29(12):1517-1529.Li Juan,Zhou Donghua,Si Xiaosheng,et al.Review of incipient fault diagnosis methods[J].Acta Automatica Sinica,2012,29(12):1517-1529.(in Chinese)
[2]Yao Chunde,Han Weiqiang,Wu Yan.Research and analysis on the air flow direction affected by check value in pressurization gas path of two electric superchargers[J].Journal of Mechanical Engineering,2012,48(6):124-129.
[3]唐贵基,王晓龙.IVMD融合奇异值差分谱的滚动轴承早期故障诊断[J].振动、测试与诊断,2016,36(4):700-707.Tang Guji,Wang Xiaolong.An incipient fault diagnosis method for rolling bearing based on improved variational mode decomposition and singular value difference spectrum[J].Journal of Vibration,Measurement&Diagnosis,2016,36(4):700-707.(in Chinese)
[4]杨庆,陈桂明,何庆飞,等.局部切空间排列算法用于轴承早期故障诊断[J].振动、测试与诊断,2012,32(5):831-835.Yang Qing,Chen Guiming,He Qingfei,et al.Inchoate fault diagnosis of rolling bearings based on local tangent space alignment algorithm[J].Journal of Vibration,Measurement&Diagnosis,2012,32(5):831-835.(in Chinese)
[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(3):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(3):478-482.(in Chinese)
[6]苏文胜,王奉涛,张志新,等.EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J].振动与冲击,2010,29(3):18-21.Su Wensheng,Wang Fengtao,Zhang Zhixin,et al.Application of EMD denoising and spectral kurtosis in early fault diagnosis of rolling element bearings[J].Journal of Vibration and Shock,2010,29(3):18-21.(in Chinese)
[7]孟宗,季艳,闫晓丽.基于DEMD和模糊熵的滚动轴承故障诊断方法研究[J].计量学报,2016,37(1):56-61.Meng Zong,Ji Yan,Yan Xiaoli.Rolling bearing fault diagnosis based on differential-based empirical mode decomposition and fuzzy entroy[J].Acta Metrologica Sinica,2016,37(1):56-61.(in Chinese)
[8]唐宏宾,吴运新,滑广军,等.基于EMD包络谱分析的液压泵故障诊断方法[J].振动与冲击,2012,31(9):44-48.Tang Hongbin,Wu Yunxin,Hua Guangjun,et al.Fault diagnosis of pump using EMD and envelope spectrum[J].Journal of Vibration and Shock,2012,31(9):44-48.(in Chinese)
[9]杨宏权,张金伟.往复式活塞隔膜泵单向阀故障自动诊断技术[J].科技资讯,2012(7):116-117.Yang Hongquan,Zhang Jinwei.The automatic diagnosis technology of the one-way valve malfunction of the reciprocating piston diaphragm pump[J].Science&Technology Information,2012(7):116-117.(in Chinese)
[10]李阳,焦宗夏,吴帅.应用单向阀配流的高频往复泵的流量特性分析及优化设计[J].机械工程学报,2013,49(14):154-163.Li Yang,Jiao Zongxia,Wu Shuai.Flow characteristics analysis and optimization design of high frequency reciprocation pump applying check value to rectification[J].Journal of Mechanical Engineering,2013,49(14):154-163.(in Chinese)
[11]冷永刚,郑安总,范胜波.SVD分量包络检测方法及其在滚动轴承早期故障诊断中的研究[J].振动工程学报,2014,27(5):794-800.Leng Yonggang,Zheng Anzong,Fan Shengbo.SVD component-envelop detection method and its application in the incipient fault diagnosis of rolling bearing[J].Journal of Vibration Engineering,2014,27(5):794-800.(in Chinese)
[12]孟宗,季艳.基于DEMD和对称差分能量算子解调的滚动轴承故障诊断[J].中国机械工程,2015,26(12):1658-1664.Meng Zong,Ji Yan.Fault diagnosis of rolling bearings based on DEMD and symmetric difference energy operator demodulation[J].China Mechanical Engineering,2015,26(12):1658-1664.(in Chinese)
[13]周小龙,姜振海,马风雷.基于改进HHT的微弱故障信号特征提取方法[J].东北电力大学学报,2016,36(5):52-56.Zhou Xiaolong,Jiang Zhenhai,Ma Fenglei.Feature extraction method for weak fault signal based on improved HHT[J].Journal of Northeast Dianli University,2016,36(5):52-56.(in Chinese)
[14]陈仁祥,汤宝平,吕中亮.基于相关系数的EEMD转子振动信号降噪方法[J].振动、测试与诊断,2012,32(4):542-546.Chen Renxiang,Tang Baoping,LüZhongliang.Denoising method of EEMD rotor vibration signal based on correlation coefficient[J].Journal of Vibration,Measurement&Diagnosis,2012,32(4):542-546.(in Chinese)
[15]韩中和,朱霄殉,李文华.基于K-L散度的EMD虚假分量识别方法研究[J].中国电机工程学报,2012,32(5):112-117.Han Zhonghe,Zhu Xiaoxun,Li Wenhua.A false component identification method of EMD based on kullback-leibler divergence[J].Proceedings of the CSEE,2012,32(5):112-117.(in Chinese)
[16]崔宝珍,马泽玮,李会龙,等.基于广义形态学滤波和Hilbert边际谱的滚动轴承故障诊断[J].中国机械工程,2016,27(11):1484-1488.Cui Baozhen,Ma Zewei,Li Huilong,et al.Fault diagnosis of rolling bearings based on generalized morphologicalfilter and Hilbert marginal spectrum[J].China Mechanical Engineering,2016,27(11):1484-1488.(in Chinese)
[2]Yao Chunde,Han Weiqiang,Wu Yan.Research and analysis on the air flow direction affected by check value in pressurization gas path of two electric superchargers[J].Journal of Mechanical Engineering,2012,48(6):124-129.
[3]唐贵基,王晓龙.IVMD融合奇异值差分谱的滚动轴承早期故障诊断[J].振动、测试与诊断,2016,36(4):700-707.Tang Guji,Wang Xiaolong.An incipient fault diagnosis method for rolling bearing based on improved variational mode decomposition and singular value difference spectrum[J].Journal of Vibration,Measurement&Diagnosis,2016,36(4):700-707.(in Chinese)
[4]杨庆,陈桂明,何庆飞,等.局部切空间排列算法用于轴承早期故障诊断[J].振动、测试与诊断,2012,32(5):831-835.Yang Qing,Chen Guiming,He Qingfei,et al.Inchoate fault diagnosis of rolling bearings based on local tangent space alignment algorithm[J].Journal of Vibration,Measurement&Diagnosis,2012,32(5):831-835.(in Chinese)
[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(3):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(3):478-482.(in Chinese)
[6]苏文胜,王奉涛,张志新,等.EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J].振动与冲击,2010,29(3):18-21.Su Wensheng,Wang Fengtao,Zhang Zhixin,et al.Application of EMD denoising and spectral kurtosis in early fault diagnosis of rolling element bearings[J].Journal of Vibration and Shock,2010,29(3):18-21.(in Chinese)
[7]孟宗,季艳,闫晓丽.基于DEMD和模糊熵的滚动轴承故障诊断方法研究[J].计量学报,2016,37(1):56-61.Meng Zong,Ji Yan,Yan Xiaoli.Rolling bearing fault diagnosis based on differential-based empirical mode decomposition and fuzzy entroy[J].Acta Metrologica Sinica,2016,37(1):56-61.(in Chinese)
[8]唐宏宾,吴运新,滑广军,等.基于EMD包络谱分析的液压泵故障诊断方法[J].振动与冲击,2012,31(9):44-48.Tang Hongbin,Wu Yunxin,Hua Guangjun,et al.Fault diagnosis of pump using EMD and envelope spectrum[J].Journal of Vibration and Shock,2012,31(9):44-48.(in Chinese)
[9]杨宏权,张金伟.往复式活塞隔膜泵单向阀故障自动诊断技术[J].科技资讯,2012(7):116-117.Yang Hongquan,Zhang Jinwei.The automatic diagnosis technology of the one-way valve malfunction of the reciprocating piston diaphragm pump[J].Science&Technology Information,2012(7):116-117.(in Chinese)
[10]李阳,焦宗夏,吴帅.应用单向阀配流的高频往复泵的流量特性分析及优化设计[J].机械工程学报,2013,49(14):154-163.Li Yang,Jiao Zongxia,Wu Shuai.Flow characteristics analysis and optimization design of high frequency reciprocation pump applying check value to rectification[J].Journal of Mechanical Engineering,2013,49(14):154-163.(in Chinese)
[11]冷永刚,郑安总,范胜波.SVD分量包络检测方法及其在滚动轴承早期故障诊断中的研究[J].振动工程学报,2014,27(5):794-800.Leng Yonggang,Zheng Anzong,Fan Shengbo.SVD component-envelop detection method and its application in the incipient fault diagnosis of rolling bearing[J].Journal of Vibration Engineering,2014,27(5):794-800.(in Chinese)
[12]孟宗,季艳.基于DEMD和对称差分能量算子解调的滚动轴承故障诊断[J].中国机械工程,2015,26(12):1658-1664.Meng Zong,Ji Yan.Fault diagnosis of rolling bearings based on DEMD and symmetric difference energy operator demodulation[J].China Mechanical Engineering,2015,26(12):1658-1664.(in Chinese)
[13]周小龙,姜振海,马风雷.基于改进HHT的微弱故障信号特征提取方法[J].东北电力大学学报,2016,36(5):52-56.Zhou Xiaolong,Jiang Zhenhai,Ma Fenglei.Feature extraction method for weak fault signal based on improved HHT[J].Journal of Northeast Dianli University,2016,36(5):52-56.(in Chinese)
[14]陈仁祥,汤宝平,吕中亮.基于相关系数的EEMD转子振动信号降噪方法[J].振动、测试与诊断,2012,32(4):542-546.Chen Renxiang,Tang Baoping,LüZhongliang.Denoising method of EEMD rotor vibration signal based on correlation coefficient[J].Journal of Vibration,Measurement&Diagnosis,2012,32(4):542-546.(in Chinese)
[15]韩中和,朱霄殉,李文华.基于K-L散度的EMD虚假分量识别方法研究[J].中国电机工程学报,2012,32(5):112-117.Han Zhonghe,Zhu Xiaoxun,Li Wenhua.A false component identification method of EMD based on kullback-leibler divergence[J].Proceedings of the CSEE,2012,32(5):112-117.(in Chinese)
[16]崔宝珍,马泽玮,李会龙,等.基于广义形态学滤波和Hilbert边际谱的滚动轴承故障诊断[J].中国机械工程,2016,27(11):1484-1488.Cui Baozhen,Ma Zewei,Li Huilong,et al.Fault diagnosis of rolling bearings based on generalized morphologicalfilter and Hilbert marginal spectrum[J].China Mechanical Engineering,2016,27(11):1484-1488.(in Chinese)