%0 Journal Article %T 基于EEMD排列熵的高速列车转向架故障特征分析<br>Fault Diagnosis of High Speed Train Bogie Based on EEMD and Permutation Entropy %A 秦 %A 娜 %A 蒋 %A 鹏 %A 孙永奎 %A 金炜东 %J 振动.测试与诊断 %D 2015 %X 高速列车转向架关键部件发生机械故障会体现在车体和转向架的振动信号中，为了从监测数据中提取非线性特征参数用于转向架故障状态的反演识别，提出基于聚合经验模态分解排列熵的特征分析方法。首先，对振动信号进行聚合经验模态分解，得到一系列窄带本征模态函数；然后，对原信号和本征模态函数分别计算排列熵值，组成多尺度的复杂性度量特征向量；最后，将高维特征向量输入最小二乘支持向量机分类识别出转向架的工作状态。仿真实验结果表明，该方法在运行速度为200km/h时，多个通道达到95%以上的识别率,验证了通过聚合经验模态分解排列熵对高速列车转向架机械故障诊断的可行性。<br>The vibration signals of the body and bogie of a high speed train are always affected by the bogie′s mechanical faults, which can be predicted using the nonlinear features extracted from the monitoring data. A new feature-analysis method is proposed based on ensemble empirical mode decomposition (EEMD) and permutation entropy. First, fault vibration signals are decomposed into a series of narrow band intrinsic mode functions (IMFs) using EEMD. Then, the permutation entropy of these IMFs and initial signals are calculated as multi-scale complexity measure feature vectors. Finally, the feature vectors are transformed in a least-squares support vector machine to classify and identify the operating conditions. Simulation and experimental results show that the recognition rate is above 95% under a running speed of 200 km/h, which proves the feasibility of this mechanical fault diagnosis method. %K 高速列车转向架 %K 特征提取 %K 聚合经验模态分解 %K 排列熵 %K 最小二乘支持向量机< %K br> %K high speed train bogie %K feature extraction %K ensemble empirical mode decomposition (EEMD) %K permutation entropy (PE) %K least squares support vector machine (LSSVM) %U http://zdcs.nuaa.edu.cn/ch/reader/view_abstract.aspx?file_no=201505013&flag=1