摘要
针对高速道岔裂纹伤损特征提取及状态监测问题,提出一种基于集合经验模态分解(ensemble empirical mode decomposition,简称EEMD)奇异熵和最小二乘支持向量机(least square support vector machine,简称LSSVM)的高速道岔裂纹伤损检测方法。首先,通过EEMD方法将非平稳的道岔振动信号自适应地分解为有限个基本模态分量(intrinsic mode function,简称IMF),每个IMF包含了原信号不同的特征尺度;然后,利用相关性分析筛选出与原始信号相关性最大的若干个IMF,计算所筛选IMF分量的奇异熵构成特征向量;最后,将多测点数据融合后的奇异熵特征向量输入LSSVM进行训练与测试,从而判断道岔的工作状态和伤损类型。模拟道岔裂纹伤损实验平台的振动信号分析及实验结果表明,在信噪比高于20dB时,该方法受噪声影响小,算法稳定性好,能有效地用于道岔裂纹伤损检测。
Considering flaw feature extraction and condition monitoring of a high-speed turnout, a turnout flaw detection method was proposed that was based on ensemble empirical mode decomposition (EEMD) singular entropy and least square support vector machine (LSSVM). First, turnout vibration signals with non-stationary characteristics were adaptively decomposed into a certain number of intrinsic mode functions (IMFs) using EEMD. Each IMF contained different feature scales of the original signal. Then, with correlation analysis, a certain number of IMFs that had the largest correlation coefficients with the original signal were sifted out. The singular entropy of these IMFs were computed and used as the feature vectors. Last, in order to classify the working state and flaw type of the turnout, the feature vectors fused with multi-point singular entropies were input into the LSSVM to train and test. The vibration signals on the turnout platform and contrast experiment were analyzed, and the results showed that this method can be effectively applied to turnout flaw detection. In addition, the proposed method was immune to noise and had stable performance when the signal-to-noise ratio was higher than 20 dB. © 2016, Editorial Department of JVMD. All right reserved.
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2016年第5期845-851,共7页
Journal of Vibration,Measurement & Diagnosis
基金
中国铁路总公司科技研究开发计划资助项目(2014X008-A
2013X012-A-1
2013X012-A-2)
国家自然科学基金资助项目(61371098)
四川省应用基础研究资助项目(2015JY0182)
关键词
裂纹检测
高速道岔
振动信号
集合经验模态分解
奇异熵
最小二乘支持向量机
Condition monitoring
Entropy
Feature extraction
Least squares approximations
Signal processing
Signal to noise ratio
Support vector machines
Vectors
