A novel convolutional neural network with multiscale cascade midpoint residual for fault diagnosis of rolling bearings

• One new 1DCNN is established by adding a midpoint residual block before the middle layer. • A multiscale cascade structure is constructed to extract features from the original data. • The feature extraction capability is improved by multiscale cascade midpoint residual. • The anti-noise robustness is greatly improved by utilizing the ELU activation function. Convolutional Neural Network (CNN) has been widely used in mechanical fault diagnosis system, and has achieved satisfactory results. However, some limitations of the number of network layers and a single fixed convolution kernel are also exposed during performing the task of fault classification. To solve these problems, this paper proposes a multiscale cascade midpoint residual convolutional neural network (MSC-MpResCNN). Firstly, a new multiscale cascade structure is introduced to extract multi-resolution features contained in the original data. Secondly, the improved midpoint residual block is adopted in each branch of multiscale cascade structure to address deep network performance degradation. In addition, exponential linear unit (ELU) replaces the original linear rectification function, which makes the noise resistance of the model stronger and increasesthe robustness and generalization. L2 weight regularization and global average pooling (GAP) are applied to the model to avoid overfitting. The feasibility of the proposed method is validated by the experiments. The results indicates that the method can obtain higher fault recognition rate compared with previous methods by multiscale cascade midpoint residual block. Furthermore, the method has great anti-noise robustness under strong noise environment.