Abstract
Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches.
Highlights
Rolling bearings are fundamental components that are widely used in rotating machinery, for example, in airplanes, machining centers, and wind turbines
dispersion entropy (DisEn), permutation entropy (PerEn), and sample entropy (SampEn) of intrinsic mode function (IMF) from ICEEMDAN are extracted to describe the characteristics of signal adaptive decomposition, nonlinear entropy analysis, and ensemble learning for multi-fault different rolling bearing fault categories; and (3) an ensemble of multi-class Support vector machine (SVM) trained with the diagnosis of the rolling bearing is systematically presented; (2) nonlinear entropy features including selected discriminative entropy features is used to accurately and intelligently classify different faults
For improved fault diagnosis of rolling bearings, a hybrid method based on ICEEMDAN, nonlinear
Summary
Rolling bearings are fundamental components that are widely used in rotating machinery, for example, in airplanes, machining centers, and wind turbines. The innovative contributions kinds of entropy features are extracted from each of the obtained IMFs. a distance-based of this work can be summarized as follows: (1) a hybrid method by the integration of nonstationary method is formulated for selecting discriminative features, and a multi-class ESVM is constructed and signal adaptive decomposition, nonlinear entropy analysis, and ensemble learning for multi-fault trained for intelligent classification of multi-fault rolling bearings. DisEn, PerEn, and SampEn of IMFs from ICEEMDAN are extracted to describe the characteristics of signal adaptive decomposition, nonlinear entropy analysis, and ensemble learning for multi-fault different rolling bearing fault categories; and (3) an ensemble of multi-class SVMs trained with the diagnosis of the rolling bearing is systematically presented; (2) nonlinear entropy features including selected discriminative entropy features is used to accurately and intelligently classify different faults.
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