Aiming at the problems of the slow model convergence and low diagnosis accuracy due to the time-series fault diagnosis data with strong noise in the industrial field， an improved one-Dimensional Convolutional and Bidirectional Long Short-Term Memory（1DCNN-BiLSTM） Neural Network fault diagnosis method was proposed. The method includes preprocessing of fault vibration signals， automatic feature extraction and vibration signal classification. Firstly， Complete Ensemble Empirical Mode Decomposition with Adaptive Noise （CEEMDAN） technology was used to preprocess the original vibration signal. Secondly， the 1DCNN-BiLSTM dual channel model was constructed， and the processed signal was input into the Bidirectional Long Short-Term Memory （BiLSTM） model channel and the One-dimensional Convolution Neural Network （1DCNN） model channel to fully extract the timing correlation characteristics， the non-correlation characteristics of the local space and the weak periodic laws of the signal. Thirdly， in response to the problem of strong noise in the signal， the Squeeze and Excitation Network （SENet） module was improved and applied to the two different channels. Finally， the features extracted from the two channels were fused by putting them into the fully connected layer， and the accurate identification of equipment faults was realized by the help of the Softmax classifier. The bearing dataset of Case Western Reserve University was used for experimental comparison and verification. The results show that after applying the improved SENet module to the 1DCNN channel and the stacked BiLSTM channel at the same time， the 1DCNN-BiLSTM dual channel model performs the highest diagnosis accuracy 96.87% with fast convergence， which is better than traditional one-channel models， thereby effectively improving the efficiency of equipment fault diagnosis.