An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis

In recent years, deep learning technique has been used in mechanical intelligent fault diagnosis and it has achieved much success. Among the deep learning models, convolutional neural network (CNN) is able to accomplish the feature learning without priori knowledge and pattern classification automatically, which makes it to be an end-to-end method. However, CNN may fall into local optimum when lack of useful information in the input signal. Diversity resolution expressions of signal in frequency domain can be obtained by using the filters with different scales (lengths) and more expressions may provide more useful information. Thus, in this paper, an improved CNN named multi-scale cascade convolutional neural network (MC-CNN) is proposed for the classification information enhancement of input. In MC-CNN, a new layer has been added before convolutional layers to construct a new signal of more distinguishable information. The composed signal is obtained by concatenating the signals convolved by original input and kernels of different lengths. To reduce the abundant neurons produced by the multi-scale signal, a convolutional layer with kernels of small size and a pooling layer are added after the multi-scale cascade layer. To verify the proposed method, the original CNNs and MC-CNN are applied to the pattern classification of bearing vibration signal with four conditions under normal and noise environments, respectively. The classification results show that the proposed MC-CNN is more effective than the commonly CNNs. In addition, the lower t-distributed stochastic neighbor embedding (t-SNE) clustered error verifies the effectiveness and necessity of MC layer further. By analyzing the kernels learned from the multi-scale cascade layer, it can be found that the kernels act as filters of different resolutions to make the frequency domain structure of different fault signals more distinguishable. By studying the influence of kernel scale in MC layer on fault diagnosis, it is found that the optimal scale does exist and will be a research emphasis in the future. Moreover, the effectiveness of MC-CNN is verified furthermore by analyzing the application of MC-CNN in bearing fault diagnosis under nonstationary working conditions.