基于EMD与邻域粗糙集的液体电磁阀故障诊断
|
摘要
液体电磁阀的故障诊断是保证飞行器动力系统正常工作、实现故障快速定位的重要手段。为了对液体电磁阀进行检测与诊断,提出了一种基于经验模态分解(EMD,Empirical Mode Decomposition)与邻域粗糙集相结合的新方法。首先对电磁阀的结构、故障形式、故障机理进行了分析,通过采集电磁阀正常、弹簧失效、阀芯卡滞、线圈异常、电气短路五种状态的驱动端电流信号,对不同状态的电流进行了分析。针对电流稳态长度难以控制,EMD分解获得的本征模态函数(IMF,Intrinsic Mode Function)分量的能量熵存在不一致的特点,选用电流变化率作为特征对其进行EMD分解。引入数据挖掘思想,采用邻域粗糙集构造贪心式属性约简算法进行属性约简,将约简后的属性集输入所设计的C4.5决策树算法,经过训练,其诊断准确率达到98%。研究结果表明:该方法能够实现液体电磁阀的快速诊断,具有一定的应用价值。
Fault diagnosis of the liquid solenoid valve is the effective measure to ensure the normal operation of aircraft power system and achieve rapid fault location. A new method based on Empirical Mode Decomposition(EMD) and neighborhood rough set is proposed for the liquid solenoid valve detection and diagnosis. Firstly, the structure, fault mode,fault mechanism of the liquid solenoid valve are analyzed, then the five conditions of the solenoid valve driving current signal are collected, including normal state, spring failure state, valve spool stuck state, coil anomaly state and electrical short circuit. Afterwards, the current characteristics of different states are analyzed. To resolve the problem of controlling difficulty that exists in the current steady state length, and the incontinuity of energy-entropy of the Intrinsic Mode Function(IMF)which is obtained by the EMD, the current rate of change is chosen as the characteristic to carry out EMD decomposition. Introducing the concept of data mining, a greedy attribute reduction algorithm is constructed using neighborhood rough sets to reduce attribution, then a diagnosis classifier is designed based on C4.5 decision tree by which the samples are trained. As a result, the diagnosis accuracy rate has reached 98%. The results show that this method can realize the fault diagnosis of the solenoid valve and have high application value.
Fault diagnosis of the liquid solenoid valve is the effective measure to ensure the normal operation of aircraft power system and achieve rapid fault location. A new method based on Empirical Mode Decomposition(EMD) and neighborhood rough set is proposed for the liquid solenoid valve detection and diagnosis. Firstly, the structure, fault mode,fault mechanism of the liquid solenoid valve are analyzed, then the five conditions of the solenoid valve driving current signal are collected, including normal state, spring failure state, valve spool stuck state, coil anomaly state and electrical short circuit. Afterwards, the current characteristics of different states are analyzed. To resolve the problem of controlling difficulty that exists in the current steady state length, and the incontinuity of energy-entropy of the Intrinsic Mode Function(IMF)which is obtained by the EMD, the current rate of change is chosen as the characteristic to carry out EMD decomposition. Introducing the concept of data mining, a greedy attribute reduction algorithm is constructed using neighborhood rough sets to reduce attribution, then a diagnosis classifier is designed based on C4.5 decision tree by which the samples are trained. As a result, the diagnosis accuracy rate has reached 98%. The results show that this method can realize the fault diagnosis of the solenoid valve and have high application value.
引文
[1]程进军,肖明清,刘帅,等.某型发动机自动测试系统设计与实现[J].计算机测量与控制,2016,24(2):61-63.
[2]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.
[3]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,2010,44(12):1747-1752.
[4]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.
[5]Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis[J].Proceedings of the Royal Society a Mathmatical Physical&Engineering Sciences,1998,454(1971):903-995.
[6]于德介,陈淼峰,程军圣,等.一种基于经验模式分解与支持向量机的转子故障诊断方法[J].中国电机工程学报,2006,26(16):162-167.
[7]李天云,赵妍,李楠.基于EMD的Hilbert变换应用于暂态信号分析[J].电力系统自动化,2005,29(4):49-52.
[8]Pawlak Z.Rough sets[J].International of Computer and Information Science,1982,11(5):341-356.
[9]范翔宇,王红卫,索中英,等.基于粗糙集--信息熵的辐射源威胁评估方法[J].北京航空航天大学学报,2016,42(8):1755-1761.
[10]胡清华,于达仁,谢宗霞.基于邻域粒化和粗糙集逼近的数值属性约简[J].软件学报,2008,19(3):640-649.
[11]陈绍炜,王聪,赵帅.决策树算法在电路故障诊断中的应用[J].计算机工程与应用,2013,49(12):233-236.
[12]王敏.电磁阀的原理及其在工程设计中的应用探讨[J].电气传动自动化,2010(5):92-94.
[13]张超,陈建军,郭迅.基于EMD能量熵和支持向量机的齿轮故障诊断方法[J].振动与冲击,2010,29(10):216-220.
[14]慕昱,夏虹,刘永阔.基于邻域粗糙集和决策树算法的核电厂故障诊断方法[J].原子能科学技术,2011,45(1):44-47.
[15]黄大吉,赵进平,苏纪兰.希尔伯特--黄变换的端点延拓[J].海洋学报,2003,25(1):1-11.
[16]郭立力,赵春江.十折交叉检验的支持向量机参数优化算法[J].计算机工程与应用,2009,45(8):55-57.
[2]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.
[3]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,2010,44(12):1747-1752.
[4]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.
[5]Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis[J].Proceedings of the Royal Society a Mathmatical Physical&Engineering Sciences,1998,454(1971):903-995.
[6]于德介,陈淼峰,程军圣,等.一种基于经验模式分解与支持向量机的转子故障诊断方法[J].中国电机工程学报,2006,26(16):162-167.
[7]李天云,赵妍,李楠.基于EMD的Hilbert变换应用于暂态信号分析[J].电力系统自动化,2005,29(4):49-52.
[8]Pawlak Z.Rough sets[J].International of Computer and Information Science,1982,11(5):341-356.
[9]范翔宇,王红卫,索中英,等.基于粗糙集--信息熵的辐射源威胁评估方法[J].北京航空航天大学学报,2016,42(8):1755-1761.
[10]胡清华,于达仁,谢宗霞.基于邻域粒化和粗糙集逼近的数值属性约简[J].软件学报,2008,19(3):640-649.
[11]陈绍炜,王聪,赵帅.决策树算法在电路故障诊断中的应用[J].计算机工程与应用,2013,49(12):233-236.
[12]王敏.电磁阀的原理及其在工程设计中的应用探讨[J].电气传动自动化,2010(5):92-94.
[13]张超,陈建军,郭迅.基于EMD能量熵和支持向量机的齿轮故障诊断方法[J].振动与冲击,2010,29(10):216-220.
[14]慕昱,夏虹,刘永阔.基于邻域粗糙集和决策树算法的核电厂故障诊断方法[J].原子能科学技术,2011,45(1):44-47.
[15]黄大吉,赵进平,苏纪兰.希尔伯特--黄变换的端点延拓[J].海洋学报,2003,25(1):1-11.
[16]郭立力,赵春江.十折交叉检验的支持向量机参数优化算法[J].计算机工程与应用,2009,45(8):55-57.