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| Based on the GADF-TL-ResNeXt Rolling Bearing Fault Diagnosis Method |
| HOU Dong-xiao1,ZHOU Zi-an1,CHENG Rong-cai1,YAN Shuang2 |
1. School of Control Engineering,Northeastern University at Qinhuangdao,Qinhuangdao,Hebei 066004,China
2.Jingneng Qinhuangdao Thermal Power Co. Ltd,Qinhuangdao,Hebei 066004,China |
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Abstract To solve the problem that traditional diagnosis methods are difficult to extract fault features effectively, a fault diagnosis method based on Gramian angular field (GAF) and TL-ResNeXt is proposed. Firstly, GAF is used to encode the original vibration signal into a two-dimensional feature map of time series correlation. Then these feature maps are input into a deeper level of packet residual network ResNeXt for automatic recognition and classification. At the same time of model training, transfer learning (TL) module is combined in the last layer of the network to accelerate the feature extraction ability of the model and fast learning. In order to verify the effectiveness of the method, the bearing data of Case Western Reserve University are compared with other methods, and the results show that the method performed best. The bearing fault data collect on the rolling mill simulation test platform show that the method also has good generalization and recognition ability under different working conditions.
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Received: 02 February 2023
Published: 10 October 2023
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2023, Vol. 44 
