Back to Search
Start Over
Fault diagnosis of rolling bearing based on back propagation neural network optimized by cuckoo search algorithm
- Source :
- Multimedia Tools and Applications. 81:1567-1587
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- 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%.
- Subjects :
- Bearing (mechanical)
Computer Networks and Communications
Computer science
Feature vector
Feature extraction
Fault (power engineering)
law.invention
Wavelet packet decomposition
Hardware and Architecture
law
Media Technology
Cuckoo search
MATLAB
Algorithm
computer
Software
Energy (signal processing)
computer.programming_language
Subjects
Details
- ISSN :
- 15737721 and 13807501
- Volume :
- 81
- Database :
- OpenAIRE
- Journal :
- Multimedia Tools and Applications
- Accession number :
- edsair.doi...........ef61d7800c758be4619fd48771909154
- Full Text :
- https://doi.org/10.1007/s11042-021-11556-x