Research Article
Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short-Term Memory
Table 2
CNN-BLSTM model parameters applied.
| Name | Filters | Kernel size/stride | Padding | Units | Activation function |
| Conv_1 | 16 | 8/2 | Same | | Elu | Pooling_1 | | 3/- | Valid | | | Conv_2 | 32 | 3/1 | Same | | Elu | Pooling_2 | | 3/- | Valid | | | Conv_3 | 64 | 3/1 | Same | | Elu | Pooling_3 | | 3/- | Valid | | | BLSTM_1 | | | | 64 | | Dense | | | | 32 | Elu | Dense | | | | 4 | Softmax |
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