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Rolling Element Bearing Performance Degradation Assessment Using Variational Mode Decomposition and Gath-Geva Clustering Time Series Segmentation
Joint Authors
Li, Hong-ru
Li, Yaolong
Wang, Bing
Gu, Hongqiang
Source
International Journal of Rotating Machinery
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-12
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
By focusing on the issue of rolling element bearing (REB) performance degradation assessment (PDA), a solution based on variational mode decomposition (VMD) and Gath-Geva clustering time series segmentation (GGCTSS) has been proposed.
VMD is a new decomposition method.
Since it is different from the recursive decomposition method, for example, empirical mode decomposition (EMD), local mean decomposition (LMD), and local characteristic-scale decomposition (LCD), VMD needs a priori parameters.
In this paper, we will propose a method to optimize the parameters in VMD, namely, the number of decomposition modes and moderate bandwidth constraint, based on genetic algorithm.
Executing VMD with the acquired parameters, the BLIMFs are obtained.
By taking the envelope of the BLIMFs, the sensitive BLIMFs are selected.
And then we take the amplitude of the defect frequency (ADF) as a degradative feature.
To get the performance degradation assessment, we are going to use the method called Gath-Geva clustering time series segmentation.
Afterwards, the method is carried out by two pieces of run-to-failure data.
The results indicate that the extracted feature could depict the process of degradation precisely.
American Psychological Association (APA)
Li, Yaolong& Li, Hong-ru& Wang, Bing& Gu, Hongqiang. 2017. Rolling Element Bearing Performance Degradation Assessment Using Variational Mode Decomposition and Gath-Geva Clustering Time Series Segmentation. International Journal of Rotating Machinery،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1169490
Modern Language Association (MLA)
Li, Yaolong…[et al.]. Rolling Element Bearing Performance Degradation Assessment Using Variational Mode Decomposition and Gath-Geva Clustering Time Series Segmentation. International Journal of Rotating Machinery No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1169490
American Medical Association (AMA)
Li, Yaolong& Li, Hong-ru& Wang, Bing& Gu, Hongqiang. Rolling Element Bearing Performance Degradation Assessment Using Variational Mode Decomposition and Gath-Geva Clustering Time Series Segmentation. International Journal of Rotating Machinery. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1169490
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1169490