Feature Extraction Method of Motor Bearing Based on Improved Empirical Mode Decomposition

ZHANG Xi-xi, GU Xing-sheng

Control Engineering of China ›› 2020, Vol. 27 ›› Issue (11) : 1882-1891.

Control Engineering of China ›› 2020, Vol. 27 ›› Issue (11) : 1882-1891.

Feature Extraction Method of Motor Bearing Based on Improved Empirical Mode Decomposition

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Abstract

Rolling bearing is an important part of the motor, but also the site prone to failure. The research on the feature extraction method of motor bearing is of great importance for fault diagnosis of motor bearing. First, permutation entropy algorithm and its improved algorithm are introduced in this paper, and are used for motor bearing fault detection. Secondly, for endpoint effect and the modal aliasing of empirical mode decomposition (EMD), a hybrid method (Modified Empirical Mode Decomposition, MEMD) combines extreme point symmetric continuation method, convergent empirical mode decomposition, decorrelation algorithm and improved permutation entropy is put forward in this paper, and is applied to the motor bearing fault feature extraction. At the end of the paper the probabilistic neural network (PNN) is applied on the motor bearing fault feature classification. The results show that using the improved empirical mode decomposition algorithm for feature extraction can significantly improve the accuracy of fault classification.

Key words

Improved permutation entropy / modal aliasing / endpoint effect / decorrelation / modified empirical mode decomposition(MEMD) / probabilistic neural network

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ZHANG Xi-xi, GU Xing-sheng. Feature Extraction Method of Motor Bearing Based on Improved Empirical Mode Decomposition[J]. Control Engineering of China, 2020, 27(11): 1882-1891

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