A variable-speed-condition bearing fault diagnosis methodology with recurrence plot coding and MobileNet-v3 model

被引:11
作者
Gu, Yingkui [1 ]
Chen, Ronghua [1 ]
Wu, Kuan [1 ]
Huang, Peng [1 ]
Qiu, Guangqi [1 ]
机构
[1] Jiangxi Univ Sci & Technol, Sch Mech & Elect Engn, Ganzhou 341000, Jiangxi, Peoples R China
基金
中国国家自然科学基金;
关键词
IDENTIFICATION; DECOMPOSITION; SIGNAL;
D O I
10.1063/5.0125548
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
To improve the quality of the non-stationary vibration features and the performance of the variable-speed-condition fault diagnosis, this paper proposed a bearing fault diagnosis approach with Recurrence Plot (RP) coding and a MobileNet-v3 model. 3500 RP images with seven fault modes were obtained with angular domain resampling technology and RP coding and were input into the MobileNet-v3 model for bearing fault diagnosis. Additionally, we performed a bearing vibration experiment to verify the effectiveness of the proposed method. The results show that the RP image coding method with 99.99% test accuracy is superior to the other three image coding methods such as Gramian Angular Difference Fields, Gramian Angular Summation Fields, and Markov Transition Fields with 96.88%, 90.20%, and 72.51%, indicating that the RP image coding method is more suitable for characterizing variable-speed fault features. Compared with four diagnosis methods such as MobileNet-v3 (small), MobileNet-v3 (large), ResNet-18, and DenseNet121, and two state-of-the-art approaches such as Symmetrized Dot Pattern and Deep Convolutional Neural Networks, RP and Convolutional Neural Networks, it is found that the proposed RP+MobileNet-v3 model has the best performance in all aspects with diagnosis accuracy, parameter numbers, and Graphics Processing Unit usage, overcoming the over-fitting phenomenon and increasing the anti-noise performance. It is concluded that the proposed RP+MobileNet-v3 model has a higher diagnostic accuracy with fewer parameters and is a lighter model.
引用
收藏
页数:14
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