Table 3 Analysis of CNN model architecture for fault detection in NEVs.
From: Enhancing fault detection in new energy vehicles via novel ensemble approach
Layer (Type) | Output Shape | Parameters |
|---|---|---|
Conv1D | (None, 10, 64) | 1,408 |
Batch Normalization | (None, 10, 64) | 256 |
Max Pooling1D | (None, 5, 64) | 0 |
Conv1D | (None, 5, 128) | 24,704 |
Batch Normalization | (None, 5, 128) | 512 |
Max Pooling1D | (None, 2, 128) | 0 |
Flatten | (None, 256) | 0 |
Dense | (None, 128) | 32,896 |
Dropout | (None, 128) | 0 |
Dense (Output Layer) | (None, 4) | 516 |
Total Parameters | 60,292 | |
Trainable Parameters | 59,908 | |
Non-trainable Parameters | 384 |
