Ge Mingtao, Wang Jie, Zhang Fangfang, Bai Ke, Ren Xiangyang
School of Electrical Engineering, Zhengzhou University, Zhengzhou 50001, China.
Entropy (Basel). 2018 Jun 11;20(6):455. doi: 10.3390/e20060455.
According to the dynamic characteristics of the rolling bearing vibration signal and the distribution characteristics of its noise, a fault identification method based on the adaptive filtering empirical wavelet transform (AFEWT) and kernel density estimation mutual information (KDEMI) classifier is proposed. First, we use AFEWT to extract the feature of the rolling bearing vibration signal. The hypothesis test of the Gaussian distribution is carried out for the sub-modes that are obtained by the twice decomposition of EWT, and Gaussian noise is filtered out according to the test results. In this way, we can overcome the noise interference and avoid the mode selection problem when we extract the feature of the signal. Then we combine the advantages of kernel density estimation (KDE) and mutual information (MI) and put forward a KDEMI classifier. The mutual information of the probability density combining the unknown signal feature vector and the probability density of the known type signal is calculated. The type of the unknown signal is determined via the value of the mutual information, so as to achieve the purpose of fault identification of the rolling bearing. In order to verify the effectiveness of AFEWT in feature extraction, we extract signal features using three methods, AFEWT, EWT, and EMD, and then use the same classifier to identify fault signals. Experimental results show that the fault signal has the highest recognition rate by using AFEWT for feature extraction. At the same time, in order to verify the performance of the AFEWT-KDEMI method, we compare two classical fault signal identification methods, SVM and BP neural network, with the AFEWT-KDEMI method. Through experimental analysis, we found that the AFEWT-KDEMI method is more stable and effective.
针对滚动轴承振动信号的动态特性及其噪声分布特性，提出了一种基于自适应滤波经验小波变换（AFEWT）和核密度估计互信息（KDEMI）分类器的故障识别方法。首先，利用AFEWT提取滚动轴承振动信号的特征。对通过经验小波变换（EWT）二次分解得到的子模态进行高斯分布的假设检验，并根据检验结果滤除高斯噪声。这样，在提取信号特征时能够克服噪声干扰，避免模式选择问题。然后结合核密度估计（KDE）和互信息（MI）的优点，提出了一种KDEMI分类器。计算未知信号特征向量的概率密度与已知类型信号的概率密度的互信息，通过互信息的值确定未知信号的类型，从而实现滚动轴承故障识别的目的。为了验证AFEWT在特征提取方面的有效性，分别采用AFEWT、EWT和EMD三种方法提取信号特征，然后使用相同的分类器对故障信号进行识别。实验结果表明，采用AFEWT提取特征时故障信号的识别率最高。同时，为了验证AFEWT-KDEMI方法的性能，将其与两种经典的故障信号识别方法——支持向量机（SVM）和BP神经网络进行比较。通过实验分析发现，AFEWT-KDEMI方法更加稳定有效。
