Cascade Convolutional Neural Network With Progressive Optimization for Motor Fault Diagnosis Under Nonstationary Conditions

Recently, convolutional neural networks (CNNs) have been successfully used for motor fault diagnosis because of its powerful feature extraction ability. However, there are still some barriers of traditional CNNs. Due to the fact of the hierarchical structure, feature resolution of CNNs will be reduced with layer growth, which can lead to the information loss. In addition, the fixed kernel size makes traditional CNNs not suitable for fault diagnosis of motors, which are widely used in nonstationary conditions. Therefore, starting from the physical characteristics of nonstationary vibration signals, a cascade CNN (C-CNN) with progressive optimization is proposed in this article. First, a cascade structure is built to avoid the information loss caused by consecutive convolution striding or pooling. Then, dilated convolution operations are implemented, which can extract the feature maps from different scales and extend the applications of CNN to nonstationary conditions. Furthermore, taking the advantage of the cascade structure, a progressive optimization algorithm is proposed for divide-and-conquer parameters optimization, which enables the C-CNN to converge to a more optimum state and improve the diagnosis performance. The proposed method is verified by two motor fault diagnosis experiments, which are conducted under constant speed and variable speed, respectively. The results show that the proposed method can achieve better performance when rotating speed is either constant or changing than exiting methods.

Automated summary

A cascade CNN (C-CNN) with progressive optimization is proposed for motor fault diagnosis in nonstationary conditions, addressing the limitations of traditional CNNs. Through physical characteristics of nonstationary vibration signals, the C-CNN achieves better performance in both constant and variable speed scenarios compared to existing methods.