Deep coupled dense convolutional network with complementary data for intelligent fault diagnosis

In recent years, artificial intelligent techniques have been extensively explored in the field of health monitoring and fault diagnosis due to their powerful capabilities. In this paper, we propose a deep coupled dense convolutional network (CDCN) with complementary data to integrate information fusion, feature extraction, and fault classification together for intelligent diagnosis. In this framework, built-in and external sensor data are first developed to form the input of network in parallel. Then, a one-dimensional CDCN is proposed, which not only could naturally build deeper network with alleviating the loss of features and gradient vanishing, but also develops a double-level information fusion strategy, including self-information fusion and mutual-information fusion, to facilitate the transmission of fault information and capture more comprehensive features. Finally, the extracted joint features are used for fault recognition and classification. The proposed approach is evaluated on a planetary gearbox test-bed. The results demonstrate the validity and superiority of the proposed method.