文章基本信息

标题：An adaptive denoising fault feature extraction method based on ensemble empirical mode decomposition and the correlation coefficient
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作者：Huixiang Yang ; Tengfei Ning ; Bangcheng Zhang 等
期刊名称：Advances in Mechanical Engineering
印刷版ISSN：1687-8140
电子版ISSN：1687-8140
出版年度：2017
卷号：9
期号：4
DOI：10.1177/1687814017696448
语种：English
出版社：Sage Publications Ltd.
摘要：Vibration signal processing is commonly used in the mechanical fault diagnosis. It contains abundant working status information. The vibration signal has some features such as non-linear and non-stationary. It has a lot of interference information. Fault information is vulnerable to the impact of the interference information. Empirical mode decomposition denoising method and kurtosis correlation threshold have been widely used in the field of fault diagnosis. But the method mainly depends on the subjective experience, the large number of attempts, and lack of adaptability. In this article, the signals are decomposed into several intrinsic mode functions adaptively with ensemble empirical mode decomposition. The intrinsic mode functions containing the main fault information are selected by the correlation coefficient to emphasize the fault feature and inhibit the normal information. Finally, the energy features of these intrinsic mode functions are taken as inputs of a neural network to identify the fault patterns of rolling bearing. The experiment shows that the neural network diagnosis method based on ensemble empirical mode decomposition has a higher fault recognition rate than based on empirical mode decomposition or wavelet packet method.
关键词：Empirical mode decomposition; ensemble empirical mode decomposition; adaptive denoising; fault feature extraction; rolling bearing