An adaptive underdamped stochastic resonance based on NN and CS for bearing fault diagnosis

Kuo Chi (), Jianshe Kang (), Fei Zhao () and Long Liu ()
Additional contact information
Kuo Chi: Army Engineering University of PLA
Jianshe Kang: Mechanical Engineering College
Fei Zhao: Northeastern University
Long Liu: Xiamen University

International Journal of System Assurance Engineering and Management, 2019, vol. 10, issue 3, No 11, 437-452

Abstract: Abstract Bearing is very important for rotating machinery, whose faults even cause the fatal accident. However, the fault-induced impulses, which are in the vibration data, are too weak to be detected. To enhance the weak impulses and detect the bearing fault, a novel adaptive underdamped stochastic resonance (AUSR) based on neural network (NN) and cuckoo search algorithm (CS) called NNCS-AUSR is proposed. In the proposed method, local signal-to-noise ratio (LSNR) is used to evaluate the AUSR output, NN to predict the range of the integral step that is one of AUSR parameters, and CS to search the optimal AUSR parameters. To verify the proposed method, bearing fault signals under different fault types, different fault levels and different motor loads are analyzed. Adaptive overdamped stochastic resonance based on CS (CS-AOSR) and AUSR based on CS (CS-AUSR) and are also used for comparison. The results show that NNCS-AUSR enhances the weak fault-induced impulses under various conditions more effectively and takes less time than CS-AOSR and CS-AUSR.

Keywords: Incipient fault diagnosis; Bearing; Underdamped stochastic resonance; Neural network; Cuckoo search algorithm (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-019-00816-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:ijsaem:v:10:y:2019:i:3:d:10.1007_s13198-019-00816-7

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-019-00816-7

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().