基于EEMD和小波包变换的早期故障敏感特征获取

提出一种基于总体平均经验模态分解和小波包变换的方法,进行早期故障敏感特征的获取,构建早期故障诊断模型. 该方法首先应用EEMD对现场采集的振动信号进行分解,分离出不同频率成分的特征信号,选择与原信号相关系数最大的 IMF分量进行信息重构;面向重构的IMF分量采用WPT进行分解,得到各个节点的小波系数;最后使用Hilbert变换提取小波包系数的包络,计算功率谱,准确获得早期故障的敏感特征. 通过对仿真信号的分析验证了该方法对故障诊断的有效性. 将该方法应用于实测的滚动轴承的内圈、外圈和滚动体故障诊断,诊断结果均表明该方法可有效提取早期故障敏感特征,故障诊断快速准确. 

Sensitive Features Extraction of Early Fault Based on EEMD and WPT

A method based on ensemble empirical mode decomposition (EEMD) and wavelet packet transform (WPT), which is used for extracting early failure sensitive features, is presented. An early fault diagnosis model is also built. According to this method, firstly vibration signals from the working site are decomposed by using EEMD into different IMFs(intrinsic mode function); the IMFs of the maximum related coefficient of IMF components and the original signal are then chosen to form the new information; Oriented IMFs, WPT decomposition is carried. each node of wavelet coefficient is obtained. They are decomposed by using WPT to obtain the wavelet coefficient of each node. Finally the envelops of wavelet packet coefficient are calculated by using Hilbert transform and the power spectrum can be used to obtain the early fault sensitive features. Effectiveness of the proposed method is verified through simulation. This method is also used for rolling bearing inner ring, outer ring faults and fault diagnosis of rolling elements. The diagnosis results indicate that the method of extracting sensitive features is effective and realizes fast and accurate fault diagnosis.