查看更多>>摘要：We have proposed a fast super-resolution (FSR) imaging based on the electromagnetic (EM) OAM (orbital angular momentum) radar. The proposed method only needs to transmit EM vortex waves twice to achieve super-resolution imaging, which greatly improves the imaging efficiency of OAM radar. The OAM modes of the transmitting vortex EM waves are opposite in the proposed method. Therefore, the proposed method does not need to use concentric uniform circular array (UCA) for controlling beams, and the complexity of the system can be simplified effectively. In this paper, the FSR imaging model has been deduced, and the FSR imaging algorithm based on pulse compression and FFT has been also proposed for reconstructing the targets. The simulation results demonstrate that our proposed method has the superior effectiveness and better performances.
查看更多>>摘要：Data-driven intelligent method has been widely used in fault diagnostics. However, it is observed that previous research studies focusing on imbalanced datasets for fault diagnosis have a limitation, that is, the number of normal and fault samples is assumed to be same or similar in the diagnosis process. This hypothesis decreases the accuracy and stability of fault diagnosis model for imbalanced datasets in practical working conditions. In this paper, a rolling bearing fault diagnosis model which combines Dual-stage Attention-based Recurrent Neural Network (DA-RNN) and Convolutional Block Attention Module (CBAM) is proposed. Firstly, the DA-RNN model is used to extend imbalanced datasets in real fault diagnosis cases. Secondly, an image processing method is designed to convert vibration signal into image by using vibration acceleration signal and its corresponding integrated velocity and displacement signals. Finally, the Convolution Neural Network (CNN) model with embedded CBAM structure is used for fault classification. Two datasets of vibration data from rolling bearings are used to evaluate the performance of the proposed methodology for fault diagnosis. Results show that the proposed DARNN-CBAM-CNN method improved fault diagnosis accuracy in 10.90%, 7.56%, 2.73% and 1.90% the performance metrics compared to a neural network based method, a machine learning based method, a deep learning based method, and a DARNN-CNN based method without using CBAM when the imbalance ratio of the dataset is 100:50. Diagnosis accuracy results of datasets with four different imbalance ratios show that the proposed method has the best performance compared to other six intelligent fault diagnosis methods, indicating that the proposed method is a promising potential for rolling bearings under imbalanced data conditions.
查看更多>>摘要：The accuracy of existing vision-based angle measurement methods is extremely reduced when the images appear defocus. To solve this problem, a robust center location method for defocus circular targets is proposed and subpixel accuracy of control points can be obtained even with defocus images. Based on this location method, two vision-based angle measurement methods are proposed to measure deflection angles for fixed-axis and movingaxis wings, respectively. These methods are simple and easy to install with no need of prior information of wings' structure or rotation axes. Simulation and real experiments show that the proposed methods have good robustness to image noise and can significantly reduce the measurement errors of defocus images. The proposed methods almost keep unchanging to the results at good focus status, and the measurement accuracy under severe defocus blur state is about 60% higher than that of the state-of-art method for comparison.
NSTL
Elsevier