A Fault Diagnosis Method of Train Wheelset Rolling Bearing Combined with Improved LMD and FK
Joint Authors
Zhang, Yu
Fan, Zhuoyou
Gao, Xiaorong
Luo, Lin
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-12-04
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Trackside acoustic signals contain intense noise and nonstationary features even after Doppler distortion correction.
Information on bearing defects in these signals is either weak or heavily attenuated.
Thus, an improved compound interpolation envelope local mean decomposition (ICIE LMD) method combined with a fast kurtogram (FK) is proposed for wheelset bearings.
In this methodology, cubic Hermite interpolation and cubic spline interpolation are employed to find the envelope of the extremal points in the ICIE LMD algorithm to improve accuracy and decrease the computing time of the decomposed signal component.
An FK is sensitive to the impact signal and extracts the fault impact features efficiently.
In the application, the proposed method uses ICIE LMD to decompose the multicomponent signal into several specific single product function (PF) components.
The kurtosis index of the PF is calculated to select the component which contains the most fault information.
Then, the selected component of PF is filtered by FK.
Finally, the squared envelope spectrum is used to obtain the fault frequency and identify the fault location.
The advantages of the ICIE LMD method are verified by simulation analysis.
In the application, the results show that the proposed method efficiently extracts the fault features and enhances the target characteristics of the sound signals from a trackside microphone array.
Furthermore, the influence of rotating frequency on locating the fault is reduced.
American Psychological Association (APA)
Zhang, Yu& Fan, Zhuoyou& Gao, Xiaorong& Luo, Lin. 2019. A Fault Diagnosis Method of Train Wheelset Rolling Bearing Combined with Improved LMD and FK. Journal of Sensors،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1191384
Modern Language Association (MLA)
Zhang, Yu…[et al.]. A Fault Diagnosis Method of Train Wheelset Rolling Bearing Combined with Improved LMD and FK. Journal of Sensors No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1191384
American Medical Association (AMA)
Zhang, Yu& Fan, Zhuoyou& Gao, Xiaorong& Luo, Lin. A Fault Diagnosis Method of Train Wheelset Rolling Bearing Combined with Improved LMD and FK. Journal of Sensors. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1191384
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1191384