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Aiming at the problems that the existing convolutional neural network cannot fully extract the correlation features between rolling bearing time domain signals, the large number of samples required for model training and the insufficient generalization, A new method for diagnosing multi-condition faults of rolling bearings based on an enhanced convolutional neural network model is proposed. The length of the bearing single-revolution fault characteristic signal is calculated according to the rolling bearing speed and sampling frequency, then the complete information of the single-revolution time domain signal is encoded by Gramian Angular Difference Field coding technology to generate the corresponding feature image, enabling the neural network can visually learn the time domain signal correlation features. The 7×7 deep convolutional layer of the ConvNeXt model is reconstructed by using the asymmetric convolution in the ACNet network model: that is, two 3×3, one 1×3 and one 3×1 asymmetric small convolution kernel are used to reconstruct the 7×7 convolutional layer in the form of a multi-branch structure combination, which enhances the feature extraction efficiency of the ConvNeXt model. The data augmentation module and learning rate decay strategy of the ConvNeXt model are improved to raise the generalization of the ConvNeX model under small-sample training, to build an enhanced deep convolutional neural network model IConvNeXt. Different fault diameters of Case Western Reserve University, composite rolling bearing faults of Southeast University and variable speed bearing fault data sets of Ottawa, Canada are used for experimental verification, the results show that the proposed IConvNeXt model achieves a fault diagnosis rate of 100% for different fault diameters and composite faults of rolling bearings, and a fault diagnosis rate of 99.63% for variable speed bearings. The proposed method is experimentally compared with RP+ResNet, RP+ IConvNeXt, time-frequency graph+DCNN, MLCNN-LSTM, MTF+ IConvNeXt and other methods, the results were condicted to validate that the fault diagnosis effect of the proposed model is better than that of other methods under less sample training and has strong generalization performance.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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