Abstract
This study proposes a novel fault diagnosis method that is based on empirical wavelet transform (EWT) and kernel density estimation classifier (KDEC), which can well diagnose fault type of the rolling element bearings. With the proposed fault diagnosis method, the vibration signal of rolling element bearing was firstly decomposed into a series of F modes by EWT, and the root mean square, kurtosis, and skewness of the F modes were computed and combined into the feature vector. According to the characteristics of kernel density estimation, a classifier based on kernel density estimation and mutual information was proposed. Then, the feature vectors were input into the KDEC for training and testing. The experimental results indicated that the proposed method can effectively identify three different operative conditions of rolling element bearings, and the accuracy rates was higher than support vector machine (SVM) classifier and back-propagation (BP) neural network classifier.
Highlights
Rolling-element bearing is one of the most widely used mechanical components in different kinds of rotating machines
Kernel density estimation classifier (KDEC) method is used for studying the distribution characteristics starting from the data; it is widely used in the engineering field due to its high efficiency, and it has no requirements for data distribution [30,31,32]
To address the shortcomings in these fault diagnosis methods, this study proposes a novel fault diagnosis method based on empirical wavelet transform (EWT) and kernel density estimation classifier (KDEC)
Summary
Rolling-element bearing is one of the most widely used mechanical components in different kinds of rotating machines. The vibration signal processing technique is one of the primary tools for rolling element bearings fault diagnosis. Due to the working environment and mechanism of the rolling bearings, the vibration signal is non-stationary and nonlinear, and it is difficult to extract the fault feature [10,11]. Kernel density estimation classifier (KDEC) method is used for studying the distribution characteristics starting from the data; it is widely used in the engineering field due to its high efficiency, and it has no requirements for data distribution [30,31,32]. A classifier based on Kernel density estimation (KDE) and mutual information is proposed to identify different fault types.
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