基于EMD小波包和ANFIS的滚动轴承故障诊断  

Application of EMD-wavelet packet and ANFIS for rolling bearing fault diagnosis

作　　者：张霆[1] 张友鹏[1] 

机构地区：[1]兰州交通大学自动化与电气工程学院,兰州730070

出　　处：《计算机工程与应用》2013年第21期230-234,共5页Computer Engineering and Applications

基　　金：甘肃省科技支撑计划(科技支甘)项目(No.1011JKCA172);兰州市科技计划项目(No.2011-1-106)

摘　　要：为了有效识别出滚动轴承的内圈故障、外圈故障、滚动体故障三种故障类型,提出一种基于经验模态分解EMD的小波包去噪和自适应神经模糊推理系统ANFIS的诊断方法。对故障信号进行去噪预处理,对已处理的信号利用ANFIS进行故障识别。结果表明,采用基于EMD的小波包去噪方法能有效地提高信噪比,在去噪的基础上,采用ANFIS进行故障诊断,诊断结果的误差低,能很好地识别出上述三种故障类型。In order to diagnose rolling bearing＇ s three fault types more effectively, such as inner race fault, outer race fault and balls fault, a method that Adaptive Neuro-Fuzzy Inference Systems （ANFIS） and wavelet packet de-noising based on Empirical Mode Decomposition（EMD） is proposed. As the signals are often corrupted by noise, so they are de-noised, and preprocessed signals are investigated using ANFIS analysis. The results show that the wavelet packet de-noising based on EMD can improve the Signal-to-Noise Ratio （SNR） effectively. After signals are preprocessed, the result of ANFIS analysis shows that average error is low. It can diagnose the three fault types above-mentioned better.

关 键 词：滚动轴承 经验模态分解 小波包去噪 自适应神经模糊推理系统 故障诊断 

分 类 号：TH113.1[机械工程—机械设计及理论]