Existing single-channel networks have poor noise immunity during fault diagnosis of rotating machinery due to the many noises associated with the operation of rotating machinery. To address this problem

a two-channel input LetNet-5 convolutional neural network model incorporating a parallel mechanism was proposed. Case Western Reserve University bearing dataset was used for the model plausibility check process

based on which Gaussian white noise with a signal-to-noise ratio of-10 dB was added to simulate the real noise situation. The shorttime Fourier transform was used to process the motor fan-side and drive-side vibration data

and the resulting timefrequency images were passed to a two-channel input LetNet-5 convolutional neural network for training and learning. The results show that

the dual-channel input LetNet-5 convolutional neural network model is able to capture the fault features in a strong noise environment well

it has higher efficiency and accuracy than the multiscale feature fusion residual model

the multimodal coupled input neural network model

the conventional K-nearest neighbour and decision tree model and the single-channel input LetNet-5 convolutional neural network model.