Journal of System Simulation
Abstract
Abstract: A rolling bearing fault diagnosis method based on a weighted domain adaptive convolutional neural network (WDACNN) is proposed to solve the problem that the data distribution of vibration signals of rolling bearings changes due to workload changes, which leads to poor generalization of fault diagnosis algorithm. In this method, the domain adaptation algorithm is embedded in the convolutional neural network to make the classifier based on the source domain achieve excellent generalization in the target domain, and the weight coefficient is introduced to weight the samples in the source domain to reduce the influence of the class weight deviation. In the simulation experiment, six migration tasks are used to verify the effectiveness of the proposed method. The average fault diagnosis accuracy of the proposed method reaches 96.6%, which proves the effectiveness of the proposed method under different workload conditions.
Recommended Citation
Zhang, Wenfeng; Zhu, Zhichao; and Wu, Dinghui
(2023)
"Rolling Bearing Fault Diagnosis Based on Weighted Domain Adaptive
Convolutional Neural Network,"
Journal of System Simulation: Vol. 35:
Iss.
11, Article 13.
DOI: 10.16182/j.issn1004731x.joss.22-0616
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss11/13
First Page
2445
Last Page
2453
CLC
TP391.9
Recommended Citation
Zhang Wenfeng, Zhu Zhichao, Wu Dinghui. Rolling Bearing Fault Diagnosis Based on Weighted Domain Adaptive Convolutional Neural Network[J]. Journal of System Simulation, 2023, 35(11): 2445-2453.
DOI
10.16182/j.issn1004731x.joss.22-0616
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons