Research on unknown fault diagnosis of rolling bearings based on parameter-adaptive maximum correlation kurtosis deconvolution

Maximum correlation kurtosis deconvolution (MCKD) is an effective means of identifying the periodic impulses of fault signals. However, the multiple input parameters required by MCKD complicate the process of fault diagnosis. To overcome this drawback, a new method for identifying fault types based...

Full description

Saved in:
Bibliographic Details
Published in:Review of scientific instruments 2021-05, Vol.92 (5), p.055103-055103
Main Authors: He, Yong, Wang, Hong, Xue, Hai, Zhang, Tao
Format: Article
Language:English
Subjects:
Correlation
Deconvolution
Entropy
Fault diagnosis
Kurtosis
Noise reduction
Parameter identification
Roller bearings
Scientific apparatus & instruments
Signal processing
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Maximum correlation kurtosis deconvolution (MCKD) is an effective means of identifying the periodic impulses of fault signals. However, the multiple input parameters required by MCKD complicate the process of fault diagnosis. To overcome this drawback, a new method for identifying fault types based on a parameter-adaptive MCKD method is proposed. First, an improved adaptive variational mode decomposition is developed to denoise the raw signal. The improved method adopts the weighted envelope entropy, which is constructed by combining the envelope entropy with the kurtosis, allowing the salience of the denoising performance to be evaluated. Furthermore, the mean maximum correlation kurtosis is constructed to allow the specification of fault types and the corresponding parameters. Finally, two rolling bearing test datasets are used to demonstrate the strong adaptability of this method compared with other adaptive techniques.
ISSN:0034-6748
1089-7623
DOI:10.1063/5.0046113