A new adaptive parallel resonance system based on cascaded feedback model of vibrational resonance and stochastic resonance and its application in fault detection of rolling bearings

Jimeng Li, Xing Cheng, Junling Peng and Zong Meng

Chaos, Solitons & Fractals, 2022, vol. 164, issue C

Abstract: Accurate extraction of weak feature information in strong background noise is a key to detect and identify rolling bearing faults. Stochastic resonance (SR) and vibrational resonance (VR) have received extensive attention and research in weak signal detection by reason of their advantages of utilizing additional inputs (i.e. noise or high frequency harmonic signals) to enhance weak signals. Considering the advantages and disadvantages of SR and VR in weak signal detection, this paper combines the two to construct a cascaded feedback model of VR and SR, and utilize it to form a parallel resonance system, which improves the detection performance of weak signals through the ensemble average effect. Furthermore, a multi-parameter optimization strategy based on the improved whale optimization algorithm (WOA) is proposed for the parameter selection of the parallel resonance system. It uses the constructed measurement index independent of the prior knowledge as the fitness function to realize automatic adjustment of multi-parameter, and obtains the final output by weighted summation of the optimal results obtained by multiple iterations. Finally, the suggested method is analyzed by numerical simulation signal and experimental data of rolling bearings, and the effectiveness and superiority of the proposed method in the detection of weak fault features are verified.

Keywords: Vibrational resonance; Parallel system; Multi-parameter optimization; Rolling bearing fault detection; Improved WOA (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077922008815
Full text for ScienceDirect subscribers only

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:eee:chsofr:v:164:y:2022:i:c:s0960077922008815

DOI: 10.1016/j.chaos.2022.112702

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().