English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

An adaptive denoising fault feature extraction method based on ensemble empirical mode decomposition and the correlation coefficient