A novel fault diagnosis method based on multi-level information fusion and hierarchical adaptive convolutional neural networks for centrifugal blowers. (November 2021) - Zhang, You; Li, Congbo; Wang, Rui; Qian, Jing
Highlights: The multi-level information fusion framework integrating temporal information, feature extraction and feature selection is proposed. Multi-level information fusion effectively captures comprehensive and complementary fault information among multi-sensor signals. Hierarchical adaptive convolutional neural network enhances the adaptive feature learning ability of the network. The proposed method shows superior diagnosis performance for centrifugal blowers. Abstract: Multi-sensor data fusion can provide abundant and complementary fault information. To improve the accuracy and robustness of diagnosis, this paper proposes a novel fault diagnosis method for centrifugal blowers based on multi-level information fusion and hierarchical adaptive convolutional neural network (HACNN). Multi-level information fusion integrates temporal information, feature extraction, feature selection into data fusion. This fusion strategy can acquire comprehensive and representative fault information from multi-sensor signals. The constructed HACNN greatly enhances the feature learning ability of the network and avoids unnecessary computational consumption by adaptive expansion. The effectiveness of the proposed method is evaluated by using datasets from a centrifugal blower test rig. The experimental results show that the testing accuracy and F1-score of the proposed method reach to 98.18%, which is obviously higher than that of CNN, DNN, DBN, BPNN and SVM in corresponding fusion method. It proves that the proposed method has superior diagnosis performance.