Abstract: 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.

Key words: Empirical Mode Decomposition（EMD）, neighborhood rough set, driving current, C4.5 decision tree, energy-entropy, fault diagnosis, attribute reduction

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。

关键词: 经验模态分解（EMD）, 邻域粗糙集, 驱动端电流, C4.5决策树, 能量熵, 故障诊断, 属性约简