基于驱动端电流检测的电磁阀故障诊断研究

doi:10.3969/j.issn.1000-1093.2014.07.023

摘要/Abstract

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。

关键词: 仪器仪表技术, 电磁换向阀, 电流检测, AMEsim, 故障诊断, 小波包分析, 前馈反向传播神经网络

Abstract: 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 energy-fault 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.

Key words: apparatus and instruments technology, electromagnetic reversing valve, current detection, AMEsim, fault diagnosis, wavelet packet analysis, feedforward-back propagation network

中图分类号:

TH137.52⁺3

刘志浩, 高钦和, 牛海龙, 管文良, 李璟玥. 基于驱动端电流检测的电磁阀故障诊断研究[J]. 兵工学报, 2014, 35(7): 1083-1090.

LIU Zhi-hao, GAO Qin-he, NIU Hai-long, GUAN Wen-liang, LI Jing-yue. The Fault Diagnosis of Electromagnetic Valves Based on Driving Current Detection[J]. Acta Armamentarii, 2014, 35(7): 1083-1090.

参考文献

［1］蔡伟,肖永超,黄先祥.基于无线传感器网络的大型武器装备液压系统状态监测研究[J].液压与气动,2009(9):14-19.
CAI Wei, XIAO Yong-chao, HUANG Xian-xiang.Research on WSN-based monitoring for hydraulic system of large weapon equipment[J]. Chinese Hydraulics & Pneumatics,2009(9):14-19.（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）

[1]	王伽豪，那文波，刘志威，昝琪，王铮. 双闭环比值控制系统传感器故障诊断方法[J]. 兵工学报, 2024, 45(4): 1252-1263.
[2]	吕卫民, 孙晨峰, 任立坤, 赵杰, 李永强. 一种基于TCN-LGBM的航空发动机气路故障诊断方法[J]. 兵工学报, 2024, 45(1): 253-263.
[3]	魏剑峰, 张发平, 卢继平, 杨向飞, 杨鹏楷. 基于高斯模型和RMSD-DS的火炮反后坐装置故障诊断方法[J]. 兵工学报, 2023, 44(10): 3101-3114.
[4]	黄文宽, 钱林方, 尹强, 刘太素. 基于迁移学习的供药装置故障诊断方法[J]. 兵工学报, 2023, 44(10): 2964-2974.
[5]	李良钰，苏铁熊，马富康，吴小军，徐春龙. 基于集合经验模态分解-支持向量机的高压共轨系统故障诊断方法[J]. 兵工学报, 2022, 43(5): 992-1001.
[6]	李泽东，李志农，陶俊勇，毛清华，张旭辉. 基于特征融合的注意力增强卷积神经网络的航空发动机滚动轴承故障诊断方法[J]. 兵工学报, 2022, 43(12): 3228-3239.
[7]	李炜，韩寅龙，孙晓静. 基于特征优选与深度学习的车载电源微小故障诊断方法[J]. 兵工学报, 2022, 43(11): 2935-2944.
[8]	李志农, 胡志峰, 毛清华, 张旭辉, 陶俊勇. 非线性调频模态分解-同步提取变换方法及其在滚动轴承故障诊断中的应用[J]. 兵工学报, 2021, 42(6): 1324-1330.
[9]	朱敏，许爱强，许晴，李睿峰. 基于改进多层核超限学习机的模拟电路故障诊断[J]. 兵工学报, 2021, 42(2): 356-369.
[10]	李佳蔚，崔涛，刘宇航，师帅楠，孙强. 基于排气温度动态模型的在线观测器研究[J]. 兵工学报, 2019, 40(8): 1562-1571.
[11]	郭伟超，赵怀山，李成，李言，汤奥斐. 基于小波包能量谱与主成分分析的轴承故障特征增强诊断方法[J]. 兵工学报, 2019, 40(11): 2370-2377.
[12]	翟禹尧，史贤俊，吕佳朋. 基于广义随机Petri网的导弹系统测试性建模与指标评估方法研究[J]. 兵工学报, 2019, 40(10): 2070-2079.
[13]	张伟，刘星，许爱强，平殿发. l_p范数约束的模拟电路3层多核故障诊断模型[J]. 兵工学报, 2018, 39(7): 1352-1363.
[14]	任海锋，潘宏侠. 基于多分形特征的枪械自动机裂纹故障诊断[J]. 兵工学报, 2018, 39(3): 457-462.
[15]	王英敏，崔涛，张付军，董天普. 基于局部线性模型树模型的柴油机进气系统漏气和堵塞故障诊断[J]. 兵工学报, 2017, 38(8): 1457-1468.