Bearing Fault Diagnosis Based on Wide Deep Convolutional Neural Network and Long Short Term Memory

Mechanical fault can cause economic loss of different degrees, even casualties. Timely fault diagnosis is an essential condition for ensuring safe production in modern industry. With the growth of intelligent manufacturing, more and more attention is paid to fault diagnosis methods that are based on deep learning. However, the diagnostic accuracy of existing diagnostic methods has still to be improved. Therefore, a fault diagnosis method called WDCNN-LSTM is proposed by combining Wide First-layer Deep Convolutional Neural Network with Long and Short Term Memory. Feature information is extracted adaptively from one-dimensional original vibration signals by Convolutional Neural Network. The extracted features are further extracted by Long and Short Term Memory, so that the fault feature information can be fully obtained. Experiments are performed on CWRU datasets to verify our proposed method. By analyzing the experimental results, we find that the average accuracy of the proposed WDCNN-LSTM model is 99.65%

Embedding of Functional Human Brain Networks on a Sphere

Human brain activity is often measured using the blood-oxygen-level dependent (BOLD) signals obtained through functional magnetic resonance imaging (fMRI). The strength of connectivity between brain regions is then measured and represented as Pearson correlation matrices. As the number of brain regions increases, the dimension of matrix increases. It becomes extremely cumbersome to even visualize and quantify such weighted complete networks. To remedy the problem, we propose to embedded brain networks onto a hypersphere, which is a Riemannian manifold with constant positive curvature