Abstract
In this paper, composite multiscale weighted permutation entropy (CMWPE) is proposed to evaluate the complexity of nonlinear time series, and the advantage of the CMWPE method is verified through analyzing the simulated signal. Meanwhile, considering the complex nonlinear dynamic characteristics of fault rolling bearing signal, a rolling bearing fault diagnosis approach based on CMWPE, joint mutual information (JMI) feature selection, and k-nearest-neighbor (KNN) classifier (CMWPE-JMI-KNN) is proposed. For CMWPE-JMI-KNN, CMWPE is utilized to extract the fault rolling bearing features, JMI is applied for sensitive features selection, and KNN classifier is employed for identifying different rolling bearing conditions. Finally, the proposed CMWPE-JMI-KNN approach is used to analyze the experimental dataset, the analysis results indicate the proposed approach could effectively identify different fault rolling bearing conditions.
Highlights
Rolling bearings are one of the most vulnerable parts in mechanical equipment, the working condition of rolling bearing has great influence on the reliability of mechanical system
In order to evaluate the complexity of time series within different scale factors, Li et al [22] proposed multiscale permutation entropy (MPE)
The results of MPE, multiscale weighted permutation entropy (MWPE), and composite multiscale weighted permutation entropy (CMWPE) on white noise are shown in Figure scale factor2.smax = 100
Summary
Rolling bearings are one of the most vulnerable parts in mechanical equipment, the working condition of rolling bearing has great influence on the reliability of mechanical system. Adaptive time-frequency signal analysis techniques such as Empirical Mode Decomposition (EMD) [5,6], Local Mean Decomposition (LMD) [7,8], and Intrinsic Time-Scale Decomposition (ITD) [9,10] are extensively employed in fault bearing feature extraction. These methods generally have the disadvantages of envelope error, mode mixing, and end effect. In order to evaluate the complexity of time series within different scale factors, Li et al [22] proposed multiscale permutation entropy (MPE). MPE was applied to extract fault features and identify different conditions for rolling bearing in [23].
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