Applications of Fractional Lower Order Synchrosqueezing Transform Time Frequency Technology to Machine Fault Diagnosis
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-19, 19 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-03
Country of Publication
Egypt
No. of Pages
19
Main Subjects
Abstract EN
Synchrosqueezing transform (SST) is a high resolution time frequency representation technology for nonstationary signal analysis.
The short time Fourier transform-based synchrosqueezing transform (FSST) and the S transform-based synchrosqueezing transform (SSST) time frequency methods are effective tools for bearing fault signal analysis.
The fault signals belong to a non-Gaussian and nonstationary alpha (α) stable distribution with 1<α<2 and even the noises being also α stable distribution.
The conventional FSST and SSST methods degenerate and even fail under α stable distribution noisy environment.
Motivated by the fact that fractional low order STFT and fractional low order S-transform work better than the traditional STFT and S-transform methods under α stable distribution noise environment, we propose in this paper the fractional lower order FSST (FLOFSST) and the fractional lower order SSST (FLOSSST).
In addition, we derive the corresponding inverse FLOSST and inverse FLOSSST.
The simulation results show that both FLOFSST and FLOSSST perform better than the conventional FSSST and SSST under α stable distribution noise in instantaneous frequency estimation and signal reconstruction.
Finally, FLOFSST and FLOSSST are applied to analyze the time frequency distribution of the outer race fault signal.
Our results show that FLOFSST and FLOSSST extract the fault features well under symmetric stable (SαS) distribution noise.
American Psychological Association (APA)
Wang, Haibin& Long, Junbo. 2020. Applications of Fractional Lower Order Synchrosqueezing Transform Time Frequency Technology to Machine Fault Diagnosis. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-19.
https://search.emarefa.net/detail/BIM-1194851
Modern Language Association (MLA)
Wang, Haibin& Long, Junbo. Applications of Fractional Lower Order Synchrosqueezing Transform Time Frequency Technology to Machine Fault Diagnosis. Mathematical Problems in Engineering No. 2020 (2020), pp.1-19.
https://search.emarefa.net/detail/BIM-1194851
American Medical Association (AMA)
Wang, Haibin& Long, Junbo. Applications of Fractional Lower Order Synchrosqueezing Transform Time Frequency Technology to Machine Fault Diagnosis. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-19.
https://search.emarefa.net/detail/BIM-1194851
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1194851