基于驱动端电流检测的电磁阀故障诊断研究
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摘要
提出基于驱动端电流检测的电磁阀故障诊断方法,研究了电磁阀驱动端电流特性及故障阀电流特征分析和识别方法。利用AMEsim软件搭建电磁阀的机、电、液模型,分析其驱动端电流与阀芯位移的关系;采集正常、弹簧断裂、阀芯轻微卡滞和阀芯完全卡死4种状态下的电流信号,分析不同状态的电流特征;针对驱动端电流为直流阶跃信号的特点,选取电流变化率为特征曲线,采用"能量-故障"的诊断方法,利用3层小波包分解对信号进行重构,并提取相应频带能量作为特征向量;利用前馈反向传播(BP)神经网络对提取的特征向量,对电磁换向阀模式识别和故障诊断。实验结果表明:基于"能量-故障"的诊断方法能较好地区分电磁阀的不同状态,并且经过训练的BP神经网络能够准确判别电磁阀的正常、弹簧断裂和阀芯卡死3种状态。
The fault diagnosis of electromagnetic valves based on driving end current detection is proposed. The current characteristics of the faulted electromagnetic valves and the failure signal are analyzed. The four conditions of the valve are detected,including normal state,spring break state,spool seizure and un-resetting state. Variation trend is the character signal on terms of the direct-current characteristic. For the trait of the current signal,the wavelet packet decomposition is used to distill the corresponding frequent band energy as feature vector. The feature database is combined with the each frequent band energy which is produced after reconfiguration. The feedforward-back propagation network is used to identify the fault type of the electromagnetic valves. The result shows that the diagnosis method of energyfault can distinguish the different conditions of the electromagnetic valves,and the feedforward-back propagation network after training can identify the 3 fault conditions. The method is an effective assistant method for the maintenance of the electromagnetic valves,which can be widely used for the fault diagnosis of other electromagnetic valves.
The fault diagnosis of electromagnetic valves based on driving end current detection is proposed. The current characteristics of the faulted electromagnetic valves and the failure signal are analyzed. The four conditions of the valve are detected,including normal state,spring break state,spool seizure and un-resetting state. Variation trend is the character signal on terms of the direct-current characteristic. For the trait of the current signal,the wavelet packet decomposition is used to distill the corresponding frequent band energy as feature vector. The feature database is combined with the each frequent band energy which is produced after reconfiguration. The feedforward-back propagation network is used to identify the fault type of the electromagnetic valves. The result shows that the diagnosis method of energyfault can distinguish the different conditions of the electromagnetic valves,and the feedforward-back propagation network after training can identify the 3 fault conditions. The method is an effective assistant method for the maintenance of the electromagnetic valves,which can be widely used for the fault diagnosis of other electromagnetic valves.
引文
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[13]贾跃,赵学涛,林贤杰,等.基于BP神经网络的鱼雷作战效能模糊综合评估模型及其仿真[J].兵工学报,2009,30(9):1232-1235.JIA Yue,ZHAO Xue-tao,LIN Xian-jie,et al.Fuzzy multifactorial evaluation model of torpedo operational effectiveness based on BP neural network and its simulation[J].Acta Armamentarii,2009,30(9):1232-1235.(in Chinese)
[2]肖永超.液压电磁阀的快速检测与故障诊断研究[D].西安:第二炮兵工程大学,2009.XIAO Yong-chao.Rapid detection and fault diagnosis of hydraulic solenoid valves[D].Xi’an:the Second Artillery Engineering Univeisity,2009.(in Chinese)
[3]周颉,蔡伟.应用时间序列双谱分析的电磁换向阀故障诊断法[J].机床与液压,2010,38(7):146-148.ZHOU Jie,CAI Wei.Fault diagnosis of electromagnetic directional valves based on time series bi-spectrum analysis[J].Machine Tool&Hydraulics,2010,38(7):146-148.(in Chinese)
[4]谢芳.支架液压阀测试系统的故障诊断与软件可靠性研究[D].成都:电子科技大学,2011.XIE Fang.Research onfault diagnosis and software reliability of the hydraulic valve testing system[D].Chengdu:University of Electronic Science and Technology of China,2011.(in Chinese)
[5]郝圣桥,许黎明,沈伟,等.电液伺服阀状态在线特征提取和异常检测方法[J].上海交通大学学报,2010,44(12):1747-1752.HAO Sheng-qiao,XU Li-ming,SHEN Wei,et al.On-line fault feature extraction and abnormality detection of electro-hydraulic servo valve's condition[J].Journal of Shanghai Jiaotong University,2010,44(12):1747-1752.(in Chinese)
[6]LI Wei,LI Wei-bo,WANG Gen-mao,et al.Research on the methods of detecting and removing slide valve failure[J].Journal of Zhejiang University Science,2000,1(1):56-60.
[7]张东来.基于驱动端电流变化的电磁控制元件故障诊断方法及装置:中国,200810216015.6[P].2008-09-09.ZHANG Dong-lai.Fault diagnostic method and device for electromagnetic control element based on drive end current changes:China,200810216015.6[P].2008-09-09.(in Chinese)
[8]张东来,马鑫.基于驱动端电流的矿用液压电磁阀缓变失效预测方法[J].电子学报,2010,38(12):2805-2809.ZHANG Dong-lai,MA Xin.Prediction method for mining hydraulic electromagnetic valve degradation failure based on driving current[J].Acta Electronica Sinica,2010,38(12):2805-2809.(in Chinese)
[9]Ma X,Zhang D L,Xu D.Degradation failure prediction from coil current signals of electromagnetic valves in coal mining based on neural network[C]∥Fifth International Conference on Natural Computation.Tianjin:IEEE,2009:208-213.
[10]李丕茂,张幽彤,倪成群,等.共轨喷油电磁阀动态特性仿真与实验[J].农业机械学报,2013,44(5):7-12.LI Pi-mao,ZHANG You-tong,NI Cheng-qun,et al.Simulation and experiment of dynamic characteristics of common-rail injector solenoid valve[J].Transactions of the Chinese Society of Agricultural Machinery,2013,44(5):7-12.(in Chinese)
[11]蔡伟,郑贤林,张志利,等.液压电磁阀故障机理分析与瞬态特性仿真[J].仪器仪表学报,2011,32(12):2726-2733.CAI Wei,ZHENG Xian-lin,ZHANG Zhi-li,et al.Failure mechanism analysis and transient characteristic s simulation of hydraulic solenoid valve[J].Chinese Journal of Scientific Instrument,2011,32(12):2726-2733.(in Chinese)
[12]李洪儒,许葆华.某型导弹发射装置液压泵故障预测研究[J].兵工学报,2009,30(7):900-906.LI Hong-ru,XU Bao-hua.Faultprognosis of hydraulic pump in the missile launcher[J].Acta Armamentarii,2009,30(7):900-906.(in Chinese)
[13]贾跃,赵学涛,林贤杰,等.基于BP神经网络的鱼雷作战效能模糊综合评估模型及其仿真[J].兵工学报,2009,30(9):1232-1235.JIA Yue,ZHAO Xue-tao,LIN Xian-jie,et al.Fuzzy multifactorial evaluation model of torpedo operational effectiveness based on BP neural network and its simulation[J].Acta Armamentarii,2009,30(9):1232-1235.(in Chinese)