Fault diagnosis of rolling bearing based on back propagation neural network optimized by cuckoo search algorithm

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12220%2F21%3A43902752" target="_blank" >RIV/60076658:12220/21:43902752 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007%2Fs11042-021-11556-x" target="_blank" >https://link.springer.com/article/10.1007%2Fs11042-021-11556-x</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11042-021-11556-x" target="_blank" >10.1007/s11042-021-11556-x</a>

Result language

angličtina

Original language name

Fault diagnosis of rolling bearing based on back propagation neural network optimized by cuckoo search algorithm

Original language description

In order to improve the accuracy of rolling bearing fault diagnosis in mechanical equipment, a new fault diagnosis method based on back propagation neural network optimized by cuckoo search algorithm is proposed. This method use the global search ability of the cuckoo search algorithm to constantly search for the best weights and thresholds, and then give it to the back propagation neural network. In this paper, wavelet packet decomposition is used for feature extraction of vibration signals. The energy values of different frequency bands are obtained through wavelet packet decomposition, and they are input as feature vectors into optimized back propagation neural network to identify different fault types of rolling bearings. Through the three sets of simulation comparison experiments of Matlab, the experimental results show that, Under the same conditions, compared with the other five models, the proposed back propagation neural network optimized by cuckoo search algorithm has the least number of training iterations and the highest diagnostic accuracy rate. And in the complex classification experiment with the same fault location but different bearing diameters, the fault recognition correct rate of the back propagation neural network optimized by cuckoo search algorithm is 96.25%.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Multimedia Tools and Applications

ISSN

1380-7501

e-ISSN

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Volume of the periodical

81

Issue of the periodical within the volume

2

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

21

Pages from-to

1567-1587

UT code for WoS article

000704942100001

EID of the result in the Scopus database

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