基于希尔伯特—黄变换的控制阀气密性故障诊断研究
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摘要
以气动薄膜控制阀气室气密性故障为研究对象,首先对控制阀阀位响应信号进行希尔伯特-黄变换,通过经验模态分解方法检测故障的发生及发生时刻;其次分析了分解所得各阶模态及其能量占比特性,获得了气动控制阀气室气密性故障类别和强度的在线诊断;最后通过模型仿真和实体阀实验验证了文章提出的检测及诊断方法的有效性和实用性;研究首次将希尔伯特黄变换信号分析方法引入到非周期、非平稳过程故障诊断中来,完整的实现了气动控制阀气室气密性故障的检测、诊断和强度识别。
Taking the airtightness of the pneumatic diaphragm control valve chamber as the research object,the Hilbert-Yellow transformation of the control valve position response signal is firstly carried out,and the occurrence and timing of the fault are detected by the empirical mode decomposition method.Secondly,the decomposition is analyzed.The obtained modes and their energy ratio characteristics are obtained,and the online diagnosis of the air tight fault category and strength of the pneumatic control valve chamber is obtained.Finally,the validity and practicability of the proposed detection and diagnosis methods are verified by model simulation and solid valve experiments.For the first time,the Hilbert Huang transform signal analysis method is introduced into the fault diagnosis of non-periodic and non-stationary processes,and the detection,diagnosis and strength identification of the air tightness of the pneumatic control valve chamber are realized.
Taking the airtightness of the pneumatic diaphragm control valve chamber as the research object,the Hilbert-Yellow transformation of the control valve position response signal is firstly carried out,and the occurrence and timing of the fault are detected by the empirical mode decomposition method.Secondly,the decomposition is analyzed.The obtained modes and their energy ratio characteristics are obtained,and the online diagnosis of the air tight fault category and strength of the pneumatic control valve chamber is obtained.Finally,the validity and practicability of the proposed detection and diagnosis methods are verified by model simulation and solid valve experiments.For the first time,the Hilbert Huang transform signal analysis method is introduced into the fault diagnosis of non-periodic and non-stationary processes,and the detection,diagnosis and strength identification of the air tightness of the pneumatic control valve chamber are realized.
引文
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[4]Mohamed K,Arashivoiu M.Real gas simulation of hydrogen release from a high pressure chamber[J].Internationl Journal of Hydrogen Energy,2005,30:903-912.
[5]Huang N E,Shem Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis[J].Proc.R.Soc.Lond,1998,A:903-995.
[6]Huang N E,Attoh-OKINE N O.Hilbert-Huang transform in engineering[M]. Singapore:World Scientific,2005:125-148.
[7]Wu Z,Huang N E.Statistical significant test of intrinsic mode functions:in Hilbert-Huang transform:introduction and applications[M].Singapore:World Scientific,2005:125-148.
[8]翟良强.鼠笼式异步电机状态监测及故障诊断方法的研究[D].衡阳:南华大学,2012.
[9]王树梁.基于HHT的提升机天轮轴承故障诊断方法研究[D].济南:山东大学,2011.
[10]段华琼,唐宾徽.基于线性多尺度模型的计算机网络数据流量预测[J].沈阳工业大学学报,2017,3:322-327.
[11]董良.汽油发动机振动测试与隔振优化研究[D].长春:吉林大学,2012.
[12]魏巍,彭涛.基于EMD模态能量分析的滚动轴承故障特征提取[J].湖南工业大学学报,2012,3:58-62.
[13]刘玉东.基于SVM的气动控制阀故障诊断[D].杭州:浙江工业大学,2016.
[2]邓传福.120-1型货车空气控制阀主阀体组成气密性检测[J].铁道机车车辆工人,2007,8:1-4.
[3]王勇,马凯.气密性检测技术在发动机生产过程中的应用[J].汽车工艺与材料,2009,(7):4-10.
[4]Mohamed K,Arashivoiu M.Real gas simulation of hydrogen release from a high pressure chamber[J].Internationl Journal of Hydrogen Energy,2005,30:903-912.
[5]Huang N E,Shem Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis[J].Proc.R.Soc.Lond,1998,A:903-995.
[6]Huang N E,Attoh-OKINE N O.Hilbert-Huang transform in engineering[M]. Singapore:World Scientific,2005:125-148.
[7]Wu Z,Huang N E.Statistical significant test of intrinsic mode functions:in Hilbert-Huang transform:introduction and applications[M].Singapore:World Scientific,2005:125-148.
[8]翟良强.鼠笼式异步电机状态监测及故障诊断方法的研究[D].衡阳:南华大学,2012.
[9]王树梁.基于HHT的提升机天轮轴承故障诊断方法研究[D].济南:山东大学,2011.
[10]段华琼,唐宾徽.基于线性多尺度模型的计算机网络数据流量预测[J].沈阳工业大学学报,2017,3:322-327.
[11]董良.汽油发动机振动测试与隔振优化研究[D].长春:吉林大学,2012.
[12]魏巍,彭涛.基于EMD模态能量分析的滚动轴承故障特征提取[J].湖南工业大学学报,2012,3:58-62.
[13]刘玉东.基于SVM的气动控制阀故障诊断[D].杭州:浙江工业大学,2016.