机械高压气动减压阀阀芯失效检测方法研究

Research on Failure Detection Method of Valve Core of Mechanical Pneumatic Pressure Reducing Valve

机械高压气动减压阀阀芯失效检测能提高气动减压阀的安全性,提出基于振动信号分析的机械高压气动减压阀阀芯失效检测方法。采用振动传感器实现对机械高压气动减压阀阀芯失效特征参数采集,构建机械高压气动减压阀阀芯失效状态下的振动传感信号特征分析模型,采用时域特征参数分析,结合频谱增益控制,实现对机械高压气动减压阀阀芯失效工况参数分析和谱特征分解,采用匹配滤波检测方法实现对机械高压气动减压阀阀芯失效振动信号的降噪处理,提取机械高压气动减压阀阀芯失效下的振动信号时频特征分量,采用振动信号的多尺度小波分解,实现对信号的关联谱特征检测,并通过多维空间融合,实现信号特征聚类,由此实现对机械高压气动减压阀阀芯失效检测。测试结果表明,采用该方法进行机械高压气动减压阀阀芯失效检测的收敛性较好,特征聚类性较强,提高了故障失效检测置信水平。

Failure detection of mechanical high-pressure pneumatic pressure relief valve spool can improve the safety of pneumatic pressure relief valve.A failure detection method of mechanical high-pressure pneumatic pressure relief valve spool based on vibration signal analysis is proposed.The vibration sensor is used to collect the failure characteristic parameters of the valve core of mechanical high-pressure pneumatic pressure reducing valve,and the vibration sensor signal characteristic analysis model under the failure state of the valve core of mechanical high-pressure pneumatic pressure reducing valve is constructed.The time domain characteristic parameter analysis and spectrum gain control are used to analyze the failure working condition parameters and decompose the spectrum characteristics of the valve core of mechanical high-pressure pneumatic pressure reducing valve,and the matched filter detection method is used to reduce the noise of the failure vibration signal of the valve core of mechanical high-pressure pneumatic pressure reducing valve.The time-frequency characteristic components of vibration signals under the failure of mechanical high-pressure pneumatic pressure relief valve spool are extracted,and the multi-scale wavelet decomposition of vibration signals is used to detect the correlation spectrum characteristics of the signals.Through multi-dimensional space fusion,the signal characteristics clustering is realized,thus realizing the failure detection of mechanical high-pressure pneumatic pressure relief valve spool.The test results show that this method has good convergence and strong feature clustering in failure detection of mechanical high-pressure pneumatic pressure relief valve spool,which improves the confidence level of failure detection.