Rolling bearing fault signals are non-smooth, non-linear, and susceptible to background noise interference. A feature layer fusion model combining multi-sensor signals and parallel attention convolutional neural networks is proposed and applied to the fault diagnosis work of rolling bearings. First, a multi-channel parallel convolutional neural network model is constructed according to the number of sensors, and the multi-sensor signals are fed to each parallel channel separately. Second, due to the different strengths of shock features within each channel and signal, the attention mechanism is introduced into each parallel channel, the fault features with strong shock characteristics are extracted, and the feature extraction capability for different sensor signals is improved. Finally, the extracted feature information is fused in the concatenate layer, the fused features are input to the fully connected layer, and the diagnosis results are output through the Softmax layer. The experimental results show that the model can effectively fuse multi-sensor signal features, and its recognition accuracy is greatly improved over that of the single sensor, providing a feasible method for bearing fault diagnosis.
Data-driven intelligent diagnosis method has been widely used in mechanical equipment fault identification. However, the data set imbalance is still one of the critical problems affecting the effect of intelligent diagnosis in the practical work of rotating machinery. To solve the problem of fault identification of rolling bearings with imbalanced data sets, a rolling bearing intelligent diagnosis method based on the ConVAE-CNN model was proposed in this paper. In this method, a convolutional variational autoencoder network (ConVAE) model is constructed. In the coding stage, the convolutional layer is used to extract the features of minority samples. In the decoding stage, full connection layers are used to expand the dataset of minority samples. Bearing fault features were extracted by convolution and pooling operations of the convolutional neural network model, and the performance of the proposed method was tested using rolling bearing experimental data. The results show that the proposed method effectively solves the bearing fault classification problem with imbalanced data sets. Compared with the other diagnosis methods, the performance of the proposed method is better, and it has a broad application prospect in the diagnosis of imbalanced data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.