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Research on Fault Diagnosis Technology of Nonintrusive Current Detection Electromagnetic valve

机译:非识别电流检测电磁阀故障诊断技术研究

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This article has carried out the research on the fault diagnosis technology of electromagnetic valve based on the current detection of the drive end, collected the drive end current signal of the electromagnetic valve under different conditions, and explored the fault pattern recognition method of the electromagnetic valve. Firstly, the research team set four typical conditions of electromagnetic valve normal operation, spring broken, spool stuck, and spool stuck slightly; secondly, the research team collect the current signal in the process of electromagnetic valve energization and pull; thirdly, the research team use wavelet packet decomposition method to decompose and reconstruct the current signal; fourthly, the energy value of each frequency band is extracted as the characteristic value of the electromagnetic valve under different conditions; fifthly, the characteristic vector of the electromagnetic valve state based on the frequency band energy is constructed; finally, the research team carry out the method based on the support vector machine to identify the electromagnetic valve fault mode. Aiming at the problem of low recognition accuracy and slow recognition speed in the traditional neural network recognition process, the electromagnetic valve fault mode recognition method based on the support vector machine is adopted. The experimental results show: (1)the method based on “entropy-fault” can realize the diagnosis of typical electromagnetic valve faults; (2)the fault diagnosis method of electromagnetic directional valve based on multi-class support vector machine can improve the recognition accuracy by 30.43% compared with the traditional neural network recognition method.
机译:本文已经对电磁阀的故障诊断技术进行了研究基于驱动端的电流检测,在不同条件下收集电磁阀的驱动端电流信号,并探索了电磁阀的故障模式识别方法。首先,研究团队设定了四个典型的电磁阀正常操作条件,弹簧破碎,羽毛卡住,丝轴略微粘着;其次,研究团队在电磁阀通电和拉动过程中收集电流信号;第三,研究团队使用小波包分解方法来分解和重​​建电流信号;第四,在不同条件下提取每个频带的能量值作为电磁阀的特性值;第五,构造了基于频带能量的电磁阀状态的特征矢量;最后,研究团队基于支持向量机进行该方法来识别电磁阀故障模式。旨在在传统的神经网络识别过程中识别精度和慢速识别速度的问题,采用了基于支撑载体机的电磁阀故障模式识别方法。实验结果表明:(1)基于“熵故障”的方法可以实现典型电磁阀故障的诊断; (2)基于多级支持向量机的电磁定向阀故障诊断方法可以通过传统的神经网络识别方法将识别精度提高30.43%。

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