Abstract
As a key component of a mechanical system, the extraction and accurate identification of rolling bearing fault feature information are of great importance to guarantee the normal operation of the mechanical system. Aiming at that the extraction of rolling bearing fault features and traditional support vector machine parameters affects the overall accuracy of pattern classification, the improved CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) time-domain energy entropy-based model for fault pattern recognition is proposed. The ICEEMDAN method is developed to decompose the signal to obtain the IMF component series. Then, the particular IMF components are selected according to the trend of correlation coefficient and variance contribution rate; meanwhile, the information entropy (power spectral entropy, singular spectral entropy, and time-domain energy entropy) of the screened IMF components is calculated to construct the feature vector sets, respectively. Finally, the feature vector sets are input into the PSO-SVM (particle swarm optimization-support vector machine) based model for training and pattern recognition. The effectiveness of the proposed method of the improved CEEMDAN time-domain energy entropy and PSO-SVM model is testified through numerical simulation and experiments on rolling bearing datasets. The comparison proceeded with the other mainstream intelligent recognition techniques indicates the superiority of the method with the diagnostic accuracy of 100% as for the final validation.
Highlights
As a critical part of rotating machinery, the condition of rolling bearings directly could affect the operation of the entire system [1]. erefore, accurate judgment of the health status of rolling bearings is essential to improve the reliability and ensure the safe operation of the equipment [2, 3]
Liu and others proposed the GA-SVM model to locate and detect bridge ripples [13]. e support vector machine (SVM) model classifier based on particle swarm optimization (PSO) is built for rolling bearing fault classification on account of the advantages of the PSO algorithm, including faster search speed, high efficiency, and strong convergence ability [14, 15]
E rest of this article is organized in the following manner: in Section 2, the theoretical foundation of the principles of the improved adaptive complete ensemble empirical mode decomposition (ICEEMDAN) algorithm, the screening criterion of the correlation coefficient-variance contribution rate, the information entropy, and the method based on the PSO-SVM model are reviewed and discussed
Summary
As a critical part of rotating machinery, the condition of rolling bearings directly could affect the operation of the entire system [1]. erefore, accurate judgment of the health status of rolling bearings is essential to improve the reliability and ensure the safe operation of the equipment [2, 3]. Shock and Vibration components generated in the signal decomposition process Based on this point, an improved self-adaptive complete ensemble empirical mode decomposition (ICEEMDAN) algorithm is developed to complete the feature extraction from the original vibration signal. E support vector machine (SVM) model classifier based on particle swarm optimization (PSO) is built for rolling bearing fault classification on account of the advantages of the PSO algorithm, including faster search speed, high efficiency, and strong convergence ability [14, 15]. E rest of this article is organized in the following manner: in Section 2, the theoretical foundation of the principles of the improved adaptive complete ensemble empirical mode decomposition (ICEEMDAN) algorithm, the screening criterion of the correlation coefficient-variance contribution rate, the information entropy, and the method based on the PSO-SVM model are reviewed and discussed.
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