Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter
Joint Authors
Chen, Chang-Zheng
Luo, Yuanqing
Kang, Shuang
Zhang, Pinyang
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-03
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
The extraction of the vibration impulse signal plays a crucial role in the fault diagnosis of rolling element bearing.
However, the detection of weak fault signals generally suffers the strong background noise.
To solve this problem, a new adaptive multiscale enhanced combination gradient morphological filter (MECGMF) is proposed for the fault diagnosis of rolling element bearing.
In this method, according to the filtering ability of four basic morphological filter operators, an enhanced combination gradient morphological operation (ECGMF) is first proposed.
This design enhances the ability of MECGMF to extract impulse signals from strong background noise.
And accordingly, a new adaptive selection strategy named kurtosis fault feature ratio (KFFR) is proposed to select an optimal structuring element (SE) scale.
Subsequently, the optimal SE scale is the largest measure of multiscale morphological filtering for extracting bearing fault information.
In the meanwhile, the effectiveness of the proposed method is verified by simulation and experiment.
Finally, the experimental results demonstrate that MECGMF can effectively restrain the noise interference and extract fault characteristic signals of rolling element bearing from strong background noise.
Moreover, comparative tests show that the proposed method is more effective in detecting wind turbine bearing failures.
American Psychological Association (APA)
Luo, Yuanqing& Chen, Chang-Zheng& Kang, Shuang& Zhang, Pinyang. 2019. Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter. Shock and Vibration،Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1211013
Modern Language Association (MLA)
Luo, Yuanqing…[et al.]. Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter. Shock and Vibration No. 2019 (2019), pp.1-16.
https://search.emarefa.net/detail/BIM-1211013
American Medical Association (AMA)
Luo, Yuanqing& Chen, Chang-Zheng& Kang, Shuang& Zhang, Pinyang. Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter. Shock and Vibration. 2019. Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1211013
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1211013