A novel one-dimensional (1-D) convolutional neural network (CNN) was proposed based on the classic model LeNet-5

aiming at problems of high computational complexity and low anti-noise ability toward rotating machinery intelligent diagnosis: (1) It adopts global average pooling layer instead of fully connected layers in the conventional CNNs

which reduces the computational complexity

model parameters and risk of overfitting

(2) It is trained with randomly dropout raw signals for anti-noise purpose and (3) It uses modified 1-D convolutional and pooling filters

which works directly on raw time-domain signals

fusing two stages of fault diagnosis into a single learning body

feature learning by the alternating convolutional and pooling layers while classification by the global average pooling layer. The bearing data and gearbox data are used in experimental verification and the classic models of LeNet-5

BP neural network and SVM are used as comparison. The results show that the adoption of global average pooling layers can reduce the model computation and improve the diagnostic accuracy under low signal-to-noise (SNR) conditions

and the train strategy of randomly dropout input can significantly improve the anti-noise ability of the model. As a result

the proposed model can realize accurate

fast and robust fault diagnosis under noisy environment. At last

the t-SNE visualization analysis is used to validate the feature learning ability of the proposed model.