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

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.