Intelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals

Shu Han, Xiaoming Liu, Yan Yang, Hailin Cao, Yuanhong Zhong and Chuanlian Luo
Additional contact information
Shu Han: School of Microelectronics and Communications Engineering, Chongqing University, Chongqing 400030, China
Xiaoming Liu: School of Microelectronics and Communications Engineering, Chongqing University, Chongqing 400030, China
Yan Yang: School of Microelectronics and Communications Engineering, Chongqing University, Chongqing 400030, China
Hailin Cao: School of Microelectronics and Communications Engineering, Chongqing University, Chongqing 400030, China
Yuanhong Zhong: School of Microelectronics and Communications Engineering, Chongqing University, Chongqing 400030, China
Chuanlian Luo: School of Microelectronics and Communications Engineering, Chongqing University, Chongqing 400030, China

Energies, 2021, vol. 14, issue 22, 1-13

Abstract: With the development of modern industry and scientific technology, production equipment plays an increasingly important role in military and industrial production, and the fault detection signal of gears and bearings state in transmission equipment becomes very important. Therefore, this paper proposes a gear-bearing composite fault signal decomposition and reconstruction method, which combines the marine predator algorithm (MPA) and variational mode decomposition (VMD) technologies. For the parameters’ selection of VMD, the optimization algorithm allows us to quickly and accurately obtain the results with the best kurtosis correlation index after signal decomposition and reconstruction. The experiments demonstrate the excellent performance of our method in the field of separation and denoising mixed gear-bearing fault signals.

Keywords: mechanical composite fault; feature separation; VMD; MAP (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/22/7702/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/22/7702/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:22:p:7702-:d:681350

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().