离心泵运行状态在线监测与故障诊断装置研制与应用
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摘要
为了准确监测离心泵的运行状态,建立了状态在线监测和故障诊断数学模型,研制了一种离心泵运行状态在线监测与故障诊断装置。该装置运行状态在线监测功能可以对离心泵的体积流量、入口压力、出口压力、扬程、功率、效率、轴向位移和径向位移进行在线监测并能够以曲线图、柱状图和数据表的形式显示。故障诊断功能包括初步诊断和精确诊断,初步诊断采用阈值对比法对汽蚀、基础松动、转子不对中和转子不平衡4种故障进行识别;精确诊断根据经验模态分解(EMD)能量熵将故障的严重程度划分为轻度、中度和重度。实际应用结果表明,该装置能够准确地对离心泵的运行状态进行在线监测和故障诊断。
In order to accurately monitor the operation state of centrifugal pumps, mathematic model for online monitoring and fault diagnosis of the operation state was established, and an online monitoring and fault diagnosis device for centrifugal pumps was also developed. With the functions of running state monitoring and fault diagnosis on line, this device can monitor the volume flow, inlet pressure, outlet pressure, pump head, power,efficiency, axial displacement and radial displacement of the centrifugal pump online. Moreover, the running state can be displayed in forms of graph, histogram and table. The fault diagnosis function includes initial diagnosis and accurate diagnosis. The initial diagnosis adopts threshold contrast method to identify the four faults of cavitation,foundation looseness, rotor misalignment and rotor imbalance. The accurate diagnosis classifies the faults as mild,moderate and severe according to the empirical mode decomposition(EMD) energy entropy. The results show that this device can monitor and diagnose the operation state of the centrifugal pump accurately.
In order to accurately monitor the operation state of centrifugal pumps, mathematic model for online monitoring and fault diagnosis of the operation state was established, and an online monitoring and fault diagnosis device for centrifugal pumps was also developed. With the functions of running state monitoring and fault diagnosis on line, this device can monitor the volume flow, inlet pressure, outlet pressure, pump head, power,efficiency, axial displacement and radial displacement of the centrifugal pump online. Moreover, the running state can be displayed in forms of graph, histogram and table. The fault diagnosis function includes initial diagnosis and accurate diagnosis. The initial diagnosis adopts threshold contrast method to identify the four faults of cavitation,foundation looseness, rotor misalignment and rotor imbalance. The accurate diagnosis classifies the faults as mild,moderate and severe according to the empirical mode decomposition(EMD) energy entropy. The results show that this device can monitor and diagnose the operation state of the centrifugal pump accurately.
引文
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[2]乔凤杰,白晶.电厂水泵故障分析及整治方法研究[J].东北电力大学学报,2015,35(5):51-55.QIAO Fengjie,BAI Jing.The failure analysis of thermal power generator set and research on improvement[J].Journal of Northeast Electric Power University,2015,35(5):51-55.
[3]王迪,姜伟,朱红波,等.故障诊断技术在离心泵中的应用分析[J].化工机械,2014,41(2):262-264.WANG Di,JIANG Wei,ZHU Hongbo,et al.Application analysis of fault diagnosis technology in centrifugal pump[J].Chemical Engineering&Machinery,2014,41(2):262-264.
[4]臧薇.基于支持向量机的煤矿井下水泵故障诊断研究[J].煤炭技术,2017,36(5):258-260.ZANG Wei.Research on fault diagnosis of coal mine water pump based on support vector machine[J].Coal Technology,2017,36(5):258-260.
[5]李洪涛.基于改进BP算法的水泵机组故障诊断的网络模型[J].机械工程与自动化,2017(1):161-163.LI Hongtao.Pump unit fault diagnosis network model based on improved BP algorithm[J].Mechanical Engineering&Automation,2017(1):161-163.
[6]陈英强,陈煜敏,蒋劲,等.基于小波包样本熵和SVM的水泵机组振动故障诊断[J].中国农村水利水电,2017(3):165-168.CHEN Yingqiang,CHEN Yumin,JIANG Jin,et al.Vibration analysis and fault diagnosis of water pump unit based on wavelet packet sample entropy and SVM[J].China Rural Water and Hydropower,2017(3):165-168.
[7]杨诗成,王喜魁.泵与风机[M].北京:中国电力出版社,2007:23-24.YANG Shicheng,WANG Xikui.Pump and fan[M].Beijing:China Electric Power Press,2007:23-24.
[8]黄忠富.小波分析在离心泵汽蚀故障诊断中的应用研究[D].南京:江苏大学,2005:26-42.HUANG Zhongfu.Research on the applications of wavelet analysis in cavitation fault diagnosis for centrifugal pumps[D].Nanjing:Jiangsu University,2005:26-42.
[9]WANG D,ZHAO Y,YI C,et al.Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings[J].Mechanical Systems and Signal Processing,2018,101:292-308.
[10]许小刚,王松岭,刘海啸.基于SDP与图像匹配的离心风机失速实时检测[J].动力工程学报,2015,35(11):906-911.XU Xiaogang,WANG Songling,LIU Haixiao.Real-time stall detection of centrifugal fans based on SDP analysis and image matching[J].Journal of Chinese Society of Power Engineering,2015,35(11):906-911.
[11]周云龙,赵鹏,孙斌.基于提升小波包的离心泵故障诊断方法[J].热能动力工程,2011,26(6):738-742.ZHOU Yunlong,ZHAO Peng,SUN Bin.Method for diagnosing the fault of a centrifugal pump based on a lifting wavelet package[J].Journal of Engineering for Thermal Energy and Power,2011,26(6):738-742.
[12]陶新民,徐晶,杜宝祥,等.基于相空间RVM的轴承故障检测方法[J].振动与冲击,2008,27(10):6-9.TAO Xinmin,XU Jing,DU Baoxiang,et al.Bearing fault detection based on RVM using phase space[J].Journal of Vibration and Shock,2008,27(10):6-9.
[13]田松峰,胥佳瑞,王美俊,等.基于EEMD云模型和SVM的汽轮机转子故障诊断方法[J].热力发电,2017,46(4):111-114.TIAN Songfeng,XU Jiarui,WANG Meijun,et al.A rotor fault diagnosis method based on EEMD cloud model and SVM[J].Thermal Power Generation,2017,46(4):111-114.
[14]周云龙,洪君,张学清,等.HHT与Elman神经网络在离心泵故障振动信号处理中的应用[J].流体机械,2007,35(5):21-24.ZHOU Yunlong,HONG Jun,ZHANG Xueqing,et al.Application of Hibert-huang transform and Elman neural network in vibration signals processing for centrifugal pump failure[J].Fluid Machinery,2007,35(5):21-24.