Abstract
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.
Highlights
Fault feature extraction is the basis of fault diagnosis, and the clear and definite fault characteristic quantity has a decisive influence on improving the accuracy and rapidity of fault diagnosis.[1,2] The quality of fault feature is the key factor to the fault diagnosis effect.[3]
An adaptive denoising fault feature extraction method based on ensemble empirical mode decomposition (EEMD) and the correlation coefficient is proposed in this article
The process of method is described in section ‘‘Rolling bearing fault diagnosis based on EEMD and neural network.’’ Section ‘‘Experimental demonstration for fault diagnosis of rolling bearing’’ describes the contrast experiment and main results
Summary
Fault feature extraction is the basis of fault diagnosis, and the clear and definite fault characteristic quantity has a decisive influence on improving the accuracy and rapidity of fault diagnosis.[1,2] The quality of fault feature is the key factor to the fault diagnosis effect.[3]. The correlation coefficient of the intrinsic mode function (IMF) component selection is the main fault information included in this method. The process of method is described in section ‘‘Rolling bearing fault diagnosis based on EEMD and neural network.’’ Section ‘‘Experimental demonstration for fault diagnosis of rolling bearing’’ describes the contrast experiment and main results.
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