An MCM-Enhanced Compressive Sensing for Weak Fault Feature Extraction of Rolling Element Bearings under Variable Speeds
Joint Authors
He, Ya
Feng, Kun
Hu, Minghui
Cui, Jinmiao
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-21, 21 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-13
Country of Publication
Egypt
No. of Pages
21
Main Subjects
Abstract EN
The compressive sensing (CS) theory provides a new slight to the big-data problem led by the Shannon sampling theorem in rolling element bearings condition monitoring, where the measurement matrix of CS tends to be designed by the random matrix (RM) to preserve the integrity of signal roughly.
However, when the signal to be analyzed is infected with strong noise, not only does the signal become insufficiently sparse, but the randomness of the measurement matrix will bring down the sensing efficiency, resulting in the loss of fault feature.
Thus, a sensing-enhanced CS scheme based on a series of modes after VMD decomposition is proposed under this paper.
The core of this scheme is as follows: (1) the principal mode of VMD with better sparsity replaces the raw signal for compressive sensing; (2) all these modes contain the time-frequency characteristics of the raw signal; (3) a new measurement matrix called mode-circulant matrix (MCM) is defined by circulating the mode matrix, and when the amount of samples is shrunk, the sensing efficiency can be enhanced greatly.
Besides, considering the fault signal of rolling bearings under variable speed, there is a need to use order tracking to overcome the nonstationarity of the signal before applying CS theory.
The analysis results of simulation and experiment prove that the VMD- and MCM-based CS can successfully extract the weak fault feature of rolling bearings with operating speed changing.
American Psychological Association (APA)
He, Ya& Feng, Kun& Hu, Minghui& Cui, Jinmiao. 2020. An MCM-Enhanced Compressive Sensing for Weak Fault Feature Extraction of Rolling Element Bearings under Variable Speeds. Shock and Vibration،Vol. 2020, no. 2020, pp.1-21.
https://search.emarefa.net/detail/BIM-1209670
Modern Language Association (MLA)
He, Ya…[et al.]. An MCM-Enhanced Compressive Sensing for Weak Fault Feature Extraction of Rolling Element Bearings under Variable Speeds. Shock and Vibration No. 2020 (2020), pp.1-21.
https://search.emarefa.net/detail/BIM-1209670
American Medical Association (AMA)
He, Ya& Feng, Kun& Hu, Minghui& Cui, Jinmiao. An MCM-Enhanced Compressive Sensing for Weak Fault Feature Extraction of Rolling Element Bearings under Variable Speeds. Shock and Vibration. 2020. Vol. 2020, no. 2020, pp.1-21.
https://search.emarefa.net/detail/BIM-1209670
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1209670