Bearing Fault Signal Analysis Based on an Adaptive Multiscale Combined Morphological Filter
Joint Authors
Li, Bing
Lv, Chun
Zhang, Peilin
Wu, Dinghai
Zhang, Yunqiang
Source
International Journal of Rotating Machinery
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-03-03
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Bearing fault signal analysis is an important means of bearing fault diagnosis.
To effectively eliminate noise in a fault signal, an adaptive multiscale combined morphological filter is proposed based on the theory of mathematical morphology.
Both simulation and experimental results show that the adaptive multiscale combined morphological filter can remove noise more thoroughly and retain details of the fault signal better than the dual-tree complex wavelet filter, traditional morphological filter, adaptive singular value decomposition method (ASVD), and improved switching Kalman filter (ISKF).
The adaptive multiscale combined morphological filter considers both positive and negative impulses in the signal; therefore, it has strong adaptability to complex noise in the environment, making it an effective new method for bearing fault diagnosis.
American Psychological Association (APA)
Lv, Chun& Zhang, Peilin& Wu, Dinghai& Li, Bing& Zhang, Yunqiang. 2020. Bearing Fault Signal Analysis Based on an Adaptive Multiscale Combined Morphological Filter. International Journal of Rotating Machinery،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1174012
Modern Language Association (MLA)
Lv, Chun…[et al.]. Bearing Fault Signal Analysis Based on an Adaptive Multiscale Combined Morphological Filter. International Journal of Rotating Machinery No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1174012
American Medical Association (AMA)
Lv, Chun& Zhang, Peilin& Wu, Dinghai& Li, Bing& Zhang, Yunqiang. Bearing Fault Signal Analysis Based on an Adaptive Multiscale Combined Morphological Filter. International Journal of Rotating Machinery. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1174012
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1174012