Fault Characteristic Extraction by Fractional Lower-Order Bispectrum Methods
Joint Authors
You, Fang
Wang, Haibin
Liu, Zeliang
Long, Junbo
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-24, 24 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-31
Country of Publication
Egypt
No. of Pages
24
Main Subjects
Abstract EN
The generated signals generally contain a large amount of background noise when the mechanical bearing fails, and the fault signals present nonlinear and non-Gaussian feature, which have heavy tail and belong to α-stable distribution (1<α<2); even the background noises are also α-stable distribution process.
Then it is difficult to obtain reliable conclusion by using the traditional bispectral analysis method under α-stable distribution environment.
Two improved bispectrum methods are proposed based on fractional lower-order covariation in this paper, including fractional low-order direct bispectrum (FLODB) method, fractional low-order indirect bispectrum (FLOIDB) method.
In order to decrease the estimate variance and increase the bispectral flatness, the fractional lower-order autoregression (FLOAR) model bispectrum and fractional lower-order autoregressive moving average (FLOARMA) model bispectrum methods are presented, and their calculation steps are summarized.
We compare the improved bispectrum methods with the conventional methods employing second-order statistics in Gaussian and SαS distribution environments; the simulation results show that the improved bispectrum methods have performance advantages compared to the traditional methods.
Finally, we use the improved methods to estimate the bispectrum of the normal and outer race fault signal; the result indicates that they are feasible and effective for fault diagnosis.
American Psychological Association (APA)
Wang, Haibin& Long, Junbo& Liu, Zeliang& You, Fang. 2020. Fault Characteristic Extraction by Fractional Lower-Order Bispectrum Methods. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-24.
https://search.emarefa.net/detail/BIM-1201610
Modern Language Association (MLA)
Wang, Haibin…[et al.]. Fault Characteristic Extraction by Fractional Lower-Order Bispectrum Methods. Mathematical Problems in Engineering No. 2020 (2020), pp.1-24.
https://search.emarefa.net/detail/BIM-1201610
American Medical Association (AMA)
Wang, Haibin& Long, Junbo& Liu, Zeliang& You, Fang. Fault Characteristic Extraction by Fractional Lower-Order Bispectrum Methods. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-24.
https://search.emarefa.net/detail/BIM-1201610
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1201610