[PERSON]  
Correspondence: [EMAIL_ADDRESS] (T.W. ); [EMAIL_ADDRESS] (X.X.) School of Mechanical Engineering, North University of China, Taiyuan 030051, China; [EMAIL_ADDRESS] (H.P. ); [EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (T.Y. ); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (H.X.) [PERSON]  
Correspondence: [EMAIL_ADDRESS] (T.W. ); [EMAIL_ADDRESS] (X.X.) [PERSON]  
Correspondence: [EMAIL_ADDRESS] (T.W. ); [EMAIL_ADDRESS] (X.X.) School of Mechanical Engineering, North University of China, Taiyuan 030051, China; [EMAIL_ADDRESS] (H.P. ); [EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (T.Y. ); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (H.X.) [PERSON]  
Correspondence: [EMAIL_ADDRESS] (T.W. ); [EMAIL_ADDRESS] (X.Z.) [PERSON]  
Correspondence: [EMAIL_ADDRESS] (T.W. ); [EMAIL_ADDRESS] (X.Z.) School of Mechanical Engineering, North University of China, Taiyuan 030051, China; [EMAIL_ADDRESS] (H.P. ); [EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (T.Y. ); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (H.X.) [PERSON]  
Correspondence: [EMAIL_ADDRESS] (T.W. ); [EMAIL_ADDRESS] (X.Z.) School of Mechanical Engineering, North University of China, Taiyuan 030051, China; [EMAIL_ADDRESS] (H.P. ); [EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (T.Y.Y. ); zx15332 extraction, which greatly lower the efficiency of fault diagnosis and cannot be widely applied and keep up with the fast technological development. With the vigorous development of computer techniques, deep learning (DL) rises in response to the proper time and conditions, and so fault diagnosis combined with DL has become popular [10; 11; 12]. [PERSON] et al. [13] proposed to combine convolutional neural networks (CNN) with batch normalization (BN) and the exponential moving average technology to solve the problems of complex working conditions and imbalanced datasets. The method has good accuracy and robustness. [PERSON] et al. [14] proposed to embed soft thresholding into the structures as nonlinear transformation layers to remove noise effects and integrate specific neural networks (NN) to adaptively adjust the threshold, which effectively improved the ability of feature learning to previous DL methods and obtain high accuracy for fault recognition of vibration signals in high noise environments. [PERSON] et al. [15] integrated dilated convolution network with a residual connection to process time domain vibration signals. The above methods combined with DL promote efficiency and decrease the complicated procedures of fault diagnosis. However, it only utilizes the signal of a single state or a single sensor for fault diagnosis, which makes it difficult to fully express the rich features contained in the raw signal. In some studies, scholars have proposed methods combining the data fusion method and DL for fault recognition [16; 17; 18]. [PERSON] et al. [19] propose to combine the signal information from three domains into the matrix with three rows and input the matrix to a pre-trained VGG-16 model, which can distinguish the different bearing defects precisely and achieve the online monitoring of early bearing failure effectively. [PERSON] et al. [20] proposed to extract permutation entropy (PE) from multi-sensor data and input multiscale permutation entropy (MPE) into multi-channel CNN (MCCNN). [PERSON] et al. [21] proposed an adaptive data fusion method based on multi-sensor data and different fusion levels. [PERSON] et al. [22] proposed to input the data obtained from multiple sensors into CNN to accomplish the end-to-end fault diagnosis. The above research on multi-sensor and multi-state data fusion has achieved many successes and raised the fault diagnosis accuracy to some extent. However, there are the following problems: (1) It is not considered that the interference information produced by the strong noise and variable working conditions will distort the signal, and the weak features will be drowned. (2) MCCNN utilized to process multi-sensor or multi-state data does not achieve complete decoupling of the channel and spatial correlation [23].